diff --git a/.gitignore b/.gitignore
index 11f5b2162..2fb733f12 100644
--- a/.gitignore
+++ b/.gitignore
@@ -15,3 +15,7 @@ testcase2/doc/build/
 parser/*.fmu
 parser/*.mo
 parser/*.json
+
+xx.ipynb
+connect_boptest.py
+\!
\ No newline at end of file
diff --git a/forecast/SimulateError.ipynb b/forecast/SimulateError.ipynb
new file mode 100644
index 000000000..b347f1c61
--- /dev/null
+++ b/forecast/SimulateError.ipynb
@@ -0,0 +1,410 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "708d1f3e",
+   "metadata": {},
+   "source": [
+    "# IBPSA - Project1\n",
+    "# Weather uncertanty emulator\n",
+    "Notebook to simulate the different uncertainty scenarios by using the function to generate the error."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "8b915178",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from ErrorEmulator import predict_error\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "852c79b4",
+   "metadata": {},
+   "source": [
+    "Low uncertainty scenario"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "026f4bbd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(50000, 48)\n"
+     ]
+    }
+   ],
+   "source": [
+    "hp = 48\n",
+    "F0_low = 0\n",
+    "K0_low = 0.6\n",
+    "F_low = 0.92\n",
+    "K_low = 0.4\n",
+    "mu_low = 0\n",
+    "Nsim = 50000\n",
+    "e_low = np.zeros((Nsim,hp))\n",
+    "for i in range(Nsim):\n",
+    "    e_low[i,:] = predict_error(hp, F0_low, K0_low, F_low, K_low, mu_low)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "aa78dd13",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Low uncertainty scenario')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mean_e_low = e_low.mean(axis=0)\n",
+    "std_e_low = e_low.std(axis=0)\n",
+    "plt.plot(mean_e_low, label='Mean error')\n",
+    "plt.plot(std_e_low, label='Std error')\n",
+    "plt.xlabel('Prediction horizon [h]')\n",
+    "plt.ylabel('Temperature [ºC]')\n",
+    "plt.legend()\n",
+    "plt.title('Low uncertainty scenario')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "750f34b2",
+   "metadata": {},
+   "source": [
+    "Intermediate uncertainty scenario - zero mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8a27d41a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "F0_int1 = 0.15\n",
+    "K0_int1 = 1.2\n",
+    "F_int1 = 0.93\n",
+    "K_int1 = 0.6\n",
+    "mu_int1 = 0\n",
+    "e_int1 = np.zeros((Nsim,hp))\n",
+    "for i in range(Nsim):\n",
+    "    e_int1[i,:] = predict_error(hp, F0_int1, K0_int1, F_int1, K_int1, mu_int1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ffb3f7a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Intermediate uncertainty scenario - nonzero mean')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mean_e_int1 = e_int1.mean(axis=0)\n",
+    "std_e_int1 = e_int1.std(axis=0)\n",
+    "plt.plot(mean_e_int1, label='Mean error')\n",
+    "plt.plot(std_e_int1, label='Std error')\n",
+    "plt.xlabel('Prediction horizon [h]')\n",
+    "plt.ylabel('Temperature [ºC]')\n",
+    "plt.legend()\n",
+    "plt.title('Intermediate uncertainty scenario - nonzero mean')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "08b11ce0",
+   "metadata": {},
+   "source": [
+    "Intermediate uncertainty scenario - nonzero mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "14cfe769",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "F0_int2 = 0.3\n",
+    "K0_int2 = 1\n",
+    "F_int2 = 0.93\n",
+    "K_int2 = 0.5\n",
+    "mu_int2 = 0.015\n",
+    "e_int2 = np.zeros((Nsim,hp))\n",
+    "for i in range(Nsim):\n",
+    "    e_int2[i,:] = predict_error(hp, F0_int2, K0_int2, F_int2, K_int2, mu_int2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "0be8e330",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Intermediate uncertainty scenario - zero mean')"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mean_e_int2 = e_int2.mean(axis=0)\n",
+    "std_e_int2 = e_int2.std(axis=0)\n",
+    "plt.plot(mean_e_int2, label='Mean error')\n",
+    "plt.plot(std_e_int2, label='Std error')\n",
+    "plt.xlabel('Prediction horizon [h]')\n",
+    "plt.ylabel('Temperature [ºC]')\n",
+    "plt.legend()\n",
+    "plt.title('Intermediate uncertainty scenario - zero mean')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bbbd856e",
+   "metadata": {},
+   "source": [
+    "High uncertainty scenario"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "146bc50f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "F0_high = -0.58\n",
+    "K0_high = 1.5\n",
+    "F_high = 0.95\n",
+    "K_high = 0.7\n",
+    "mu_high = -0.015\n",
+    "e_high = np.zeros((Nsim,hp))\n",
+    "for i in range(Nsim):\n",
+    "    e_high[i,:] = predict_error(hp, F0_high, K0_high, F_high, K_high, mu_high)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "cf95f4f9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'High uncertainty scenario')"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mean_e_high = e_high.mean(axis=0)\n",
+    "std_e_high = e_high.std(axis=0)\n",
+    "plt.plot(mean_e_high, label='Mean error')\n",
+    "plt.plot(std_e_high, label='Std error')\n",
+    "plt.xlabel('Prediction horizon [h]')\n",
+    "plt.ylabel('Temperature [ºC]')\n",
+    "plt.legend()\n",
+    "plt.title('High uncertainty scenario')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "776efb80",
+   "metadata": {},
+   "source": [
+    "Comparison of the scenarios"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "9a835fd3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x17f3aad5d68>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tp = range(1,49)\n",
+    "color_low = [0.4660, 0.6740, 0.1880]\n",
+    "color_int1 = [0.1529, 0.6078, 0.9098]\n",
+    "color_int2 = [0, 0.4470, 0.7410]\n",
+    "color_high = [0.6350, 0.0780, 0.1840]\n",
+    "plt.plot(tp,mean_e_low, label='Low uncertainty', color=color_low)\n",
+    "plt.plot(tp,mean_e_int1, label='Intermediate uncertainty - zero mean', color=color_int1)\n",
+    "plt.plot(tp,mean_e_int2, label='Intermediate uncertinty - nonzero mean', color=color_int2)\n",
+    "plt.plot(tp,mean_e_high, label='High uncertainty', color=color_high)\n",
+    "plt.xlabel('Prediction horizon [h]')\n",
+    "plt.ylabel('Mean temperature error [ºC]')\n",
+    "plt.title('Mean error')\n",
+    "plt.legend(bbox_to_anchor=(1.05,1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "f377ea2a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x17f3a6d2e10>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(tp,std_e_low, label='Low uncertainty',color=color_low)\n",
+    "plt.plot(tp,std_e_int1, label='Intermediate uncertainty - zero mean',color=color_int1)\n",
+    "plt.plot(tp,std_e_int2, label='Intermediate uncertinty - nonzero mean',color=color_int2)\n",
+    "plt.plot(tp,std_e_high, label='High uncertainty',color=color_high)\n",
+    "plt.xlabel('Prediction horizon [h]')\n",
+    "plt.ylabel('Standard deviation temperature error [ºC]')\n",
+    "plt.title('STD error')\n",
+    "plt.legend(bbox_to_anchor=(1.05,1))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/forecast/error_emulator.py b/forecast/error_emulator.py
new file mode 100644
index 000000000..9ff5a69d9
--- /dev/null
+++ b/forecast/error_emulator.py
@@ -0,0 +1,95 @@
+'''
+Created on Sept 5, 2022
+
+@author: Laura Zabala
+
+'''
+
+import numpy as np
+
+
+
+def mean_filter(data, window_size=3):
+    """
+    Apply mean filter to a 1D list of data.
+
+    Parameters:
+    - data: List of numbers to be filtered.
+    - window_size: Size of the filtering window. Must be an odd number.
+
+    Returns:
+    - Filtered data.
+    """
+    if window_size % 2 == 0:
+        raise ValueError("Window size must be an odd number.")
+
+    half_window = window_size // 2
+    filtered_data = data.copy()
+
+    for i in range(half_window, len(data) - half_window):
+        if data[i] == 0:
+            continue
+
+        window_data = data[i - half_window : i + half_window + 1]
+        filtered_data[i] = sum(window_data) / len(window_data)
+
+    return filtered_data
+def predict_temperature_error_AR1(hp, F0, K0, F, K, mu):
+    '''
+    Generates an error for the temperature forecast with an AR model with normal distribution in the hp points of the predictions horizon.
+
+        Parameters
+        ----------
+        hp : int
+            Number of points in the prediction horizon.
+        F0 : float
+            Mean of the initial error model.
+        K0 : float
+            Standard deviation of the initial error model.
+        F : float
+            Autocorrelation factor of the AR error model, value should be between 0 and 1.
+        K : float
+            Standard deviation of the AR error model.
+        mu : float
+            Mean value of the distribution function integrated in the AR error model.
+
+
+        Returns
+        -------
+        error : 1D array
+            Array containing the error values in the hp points.
+    '''
+
+
+    error = np.zeros(hp)
+    error[0] = np.random.normal(F0, K0)
+    for i_c in range(hp - 1):
+        error[i_c + 1] = np.random.normal(
+            error[i_c] * F + mu, K
+        )
+    return error
+
+
+
+def predict_solar_error_AR1(hp, ag0, bg0, phi, ag, bg):
+    '''Generates an error for the solar forecast based on the specified parameters using an AR model with Laplace distribution in the hp points of the predictions horizon.
+
+        Parameters
+        ----------
+        hp : int
+            Number of points in the prediction horizon.
+        ag0, bg0, phi, ag, bg : float
+            Parameters for the AR1 model.
+
+        Returns
+        -------
+        error : 1D numpy array
+            Contains the error values in the hp points.
+    '''
+
+    error = np.zeros(hp)
+    error[0] = np.random.laplace(ag0, bg0)
+    for i_c in range(1, hp):
+        error[i_c] = np.random.laplace(error[i_c - 1] * phi + ag, bg)
+
+    return error
diff --git a/forecast/forecast_uncertainty_params.json b/forecast/forecast_uncertainty_params.json
new file mode 100644
index 000000000..f2b99db71
--- /dev/null
+++ b/forecast/forecast_uncertainty_params.json
@@ -0,0 +1,49 @@
+{
+    "temperature": {
+        "low": {
+            "F0": 0,
+            "K0": 0.6,
+            "F": 0.92,
+            "K": 0.4,
+            "mu": 0
+        },
+        "medium": {
+            "F0": 0.15,
+            "K0": 1.2,
+            "F": 0.93,
+            "K": 0.6,
+            "mu": 0
+        },
+        "high": {
+            "F0": -0.58,
+            "K0": 1.5,
+            "F": 0.95,
+            "K": 0.7,
+            "mu": -0.015
+        }
+    },
+    "solar": {
+        "low": {
+            "ag0": 4.44,
+            "bg0": 57.42,
+            "phi": 0.62,
+            "ag": 1.86,
+            "bg": 45.64
+        },
+        "medium": {
+            "ag0": 15.02,
+            "bg0": 122.6,
+            "phi": 0.63,
+            "ag": 4.44,
+            "bg": 91.97
+        },
+        "high": {
+            "ag0": 32.09,
+            "bg0": 119.94,
+            "phi": 0.67,
+            "ag": 10.63,
+            "bg": 87.44
+        }
+    }
+
+}
diff --git a/forecast/forecaster.py b/forecast/forecaster.py
index 0cc428c52..de4d681b2 100644
--- a/forecast/forecaster.py
+++ b/forecast/forecaster.py
@@ -1,13 +1,16 @@
 '''
 Created on Apr 25, 2019
 
-@author: Javier Arroyo
+@author: Javier Arroyo, Laura Zabala and Wanfu Zheng
 
 This module contains the Forecaster class with methods to obtain
 forecast data for the test case. It relies on the data_manager object
 of the test case to provide deterministic forecast.
 
 '''
+from .error_emulator import predict_temperature_error_AR1, predict_solar_error_AR1, mean_filter
+import numpy as np
+
 
 class Forecaster(object):
     '''This class retrieves test case data forecast for its use in
@@ -30,45 +33,146 @@ def __init__(self, testcase):
         # Point to the test case object
         self.case = testcase
 
-    def get_forecast(self,point_names, horizon=24*3600, interval=3600,
-                     category=None, plot=False):
-        '''Returns forecast of the test case data
+    def get_forecast(self, point_names, horizon=24 * 3600, interval=3600,
+                     wea_tem_dry_bul=None, wea_sol_glo_hor=None, seed=None):
+        '''
+        Retrieves forecast data for specified points over a given horizon and interval.
 
         Parameters
         ----------
         point_names : list of str
-            List of forecast point names for which to get data.
-        horizon : int, default is 86400 (one day)
-            Length of the requested forecast in seconds. If None,
-            the test case horizon will be used instead.
-        interval : int, default is 3600 (one hour)
-            resampling time interval in seconds. If None,
-            the test case interval will be used instead.
-        category : string, default is None
-            Type of data to retrieve from the test case.
-            If None it will return all available test case
-            data without filtering it by any category.
-            Possible options are 'weather', 'prices',
-            'emissions', 'occupancy', internalGains, 'setpoints'
-        plot : boolean, default is False
-            True if desired to plot the forecast
+            List of data point names for which the forecast is to be retrieved.
+        horizon : int, optional
+            Forecast horizon in seconds (default is 86400 seconds, i.e., one day).
+        interval : int, optional
+            Time interval between forecast points in seconds (default is 3600 seconds, i.e., one hour).
+        wea_tem_dry_bul : dict, optional
+            Parameters for the AR1 model to simulate forecast error in dry bulb temperature:
+                - F0, K0, F, K, mu : parameters used in the AR1 model.
+            If None, defaults to a dictionary with all parameters set to zero, simulating no forecast error.
+        wea_sol_glo_hor : dict, optional
+            Parameters for the AR1 model to simulate forecast error in global horizontal solar irradiation:
+                - ag0, bg0, phi, ag, bg : parameters used in the AR1 model.
+            If None, defaults to a dictionary with all parameters set to zero, simulating no forecast error.
+        seed : int, optional
+            Seed for the random number generator to ensure reproducibility of the stochastic forecast error.
 
         Returns
         -------
         forecast : dict
-            Dictionary with the requested forecast data
-            {<variable_name>:<variable_forecast_trajectory>}
-            where <variable_name> is a string with the variable
-            key and <variable_forecast_trajectory> is a list with
-            the forecasted values. 'time' is included as a variable
+            A dictionary containing the forecast data for the requested points with applied error models.
+=======
+                     weather_temperature_dry_bulb=None, weather_solar_global_horizontal=None, seed=None,
+                     category=None, plot=False):
 
-        '''
+        if weather_temperature_dry_bulb is None:
+            weather_temperature_dry_bulb = {
+                "F0": 0, "K0": 0, "F": 0, "K": 0, "mu": 0
+            }
 
+        if weather_solar_global_horizontal is None:
+            weather_solar_global_horizontal = {
+                "ag0": 0, "bg0": 0, "phi": 0, "ag": 0, "bg": 0
+            }
         # Get the forecast
         forecast = self.case.data_manager.get_data(variables=point_names,
                                                    horizon=horizon,
-                                                   interval=interval,
-                                                   category=category,
-                                                   plot=plot)
+                                                   interval=interval)
+
+        if 'TDryBul' in point_names and any(wea_tem_dry_bul.values()):
+            if seed is not None:
+                np.random.seed(seed)
+            # error in the forecast
+            error_forecast_temp = predict_temperature_error_AR1(
+                hp=int(horizon / interval + 1),
+                F0=wea_tem_dry_bul["F0"],
+                K0=wea_tem_dry_bul["K0"],
+                F=wea_tem_dry_bul["F"],
+                K=wea_tem_dry_bul["K"],
+                mu=wea_tem_dry_bul["mu"]
+            )
+
+            # forecast error just added to dry bulb temperature
+            forecast['TDryBul'] = forecast['TDryBul'] - error_forecast_temp
+            forecast['TDryBul'] = forecast['TDryBul'].tolist()
+        if 'HGloHor' in point_names and any(wea_sol_glo_hor.values()):
+
+            original_HGloHor = np.array(forecast['HGloHor']).copy()
+            lower_bound = 0.2 * original_HGloHor
+            upper_bound = 2 * original_HGloHor
+            indices = np.where(original_HGloHor > 50)[0]
+
+
+            for i in range(200):
+                if seed is not None:
+                    np.random.seed(seed+i*i)
+                error_forecast_solar = predict_solar_error_AR1(
+                    int(horizon / interval + 1),
+                    wea_sol_glo_hor["ag0"],
+                    wea_sol_glo_hor["bg0"],
+                    wea_sol_glo_hor["phi"],
+                    wea_sol_glo_hor["ag"],
+                    wea_sol_glo_hor["bg"]
+                )
+
+                forecast['HGloHor'] = original_HGloHor - error_forecast_solar
+
+                # Check if any point in forecast['HGloHor'] is out of the specified range
+                condition = np.any((forecast['HGloHor'][indices] > 2 * original_HGloHor[indices]) |
+                                   (forecast['HGloHor'][indices] < 0.2 * original_HGloHor[indices]))
+                # forecast['HGloHor']=gaussian_filter_ignoring_nans(forecast['HGloHor'])
+                forecast['HGloHor'] = mean_filter(forecast['HGloHor'])
+                forecast['HGloHor'] = np.clip(forecast['HGloHor'], lower_bound, upper_bound)
+                forecast['HGloHor'] = forecast['HGloHor'].tolist()
+                if not condition:
+                    break
+
+        if 'TDryBul' in point_names and any(weather_temperature_dry_bulb.values()):
+            if seed is not None:
+                np.random.seed(seed)
+            # error in the forecast
+            error_forecast_temp = predict_temperature_error_AR1(
+                hp=int(horizon / interval + 1),
+                F0=weather_temperature_dry_bulb["F0"],
+                K0=weather_temperature_dry_bulb["K0"],
+                F=weather_temperature_dry_bulb["F"],
+                K=weather_temperature_dry_bulb["K"],
+                mu=weather_temperature_dry_bulb["mu"]
+            )
+
+            # forecast error just added to dry bulb temperature
+            forecast['TDryBul'] = forecast['TDryBul'] - error_forecast_temp
+            forecast['TDryBul'] = forecast['TDryBul'].tolist()
+        if 'HGloHor' in point_names and any(weather_solar_global_horizontal.values()):
+
+            original_HGloHor = np.array(forecast['HGloHor']).copy()
+            lower_bound = 0.2 * original_HGloHor
+            upper_bound = 2 * original_HGloHor
+            indices = np.where(original_HGloHor > 50)[0]
+
+
+            for i in range(200):
+                if seed is not None:
+                    np.random.seed(seed+i*i)
+                error_forecast_solar = predict_solar_error_AR1(
+                    int(horizon / interval + 1),
+                    weather_solar_global_horizontal["ag0"],
+                    weather_solar_global_horizontal["bg0"],
+                    weather_solar_global_horizontal["phi"],
+                    weather_solar_global_horizontal["ag"],
+                    weather_solar_global_horizontal["bg"]
+                )
+
+                forecast['HGloHor'] = original_HGloHor - error_forecast_solar
+
+                # Check if any point in forecast['HGloHor'] is out of the specified range
+                condition = np.any((forecast['HGloHor'][indices] > 2 * original_HGloHor[indices]) |
+                                   (forecast['HGloHor'][indices] < 0.2 * original_HGloHor[indices]))
+                # forecast['HGloHor']=gaussian_filter_ignoring_nans(forecast['HGloHor'])
+                forecast['HGloHor'] = mean_filter(forecast['HGloHor'])
+                forecast['HGloHor'] = np.clip(forecast['HGloHor'], lower_bound, upper_bound)
+                forecast['HGloHor'] = forecast['HGloHor'].tolist()
+                if not condition:
+                    break
 
         return forecast
diff --git a/restapi.py b/restapi.py
index 50f6655c1..7a98f6897 100644
--- a/restapi.py
+++ b/restapi.py
@@ -82,12 +82,23 @@ def handle_validation_error(self, error, bundle_errors):
 parser_scenario = reqparse.RequestParser(argument_class=CustomArgument)
 parser_scenario.add_argument('electricity_price', type=str)
 parser_scenario.add_argument('time_period', type=str)
+parser_scenario.add_argument('temperature_uncertainty', type=str)
+parser_scenario.add_argument('solar_uncertainty', type=str)
+parser_scenario.add_argument('seed', type=int)
 # ``forecast`` interface
 parser_forecast_points = reqparse.RequestParser(argument_class=CustomArgument)
 parser_forecast_points.add_argument('point_names', type=list, action='append', required=True)
-forecast_parameters = ['horizon', 'interval']
-for arg in forecast_parameters:
-    parser_forecast_points.add_argument(arg, required=True)
+
+forecast_parameters = ['horizon', 'interval', 'temperature_uncertainty', 'solar_uncertainty']
+
+# Add required parameters
+parser_forecast_points.add_argument('horizon', required=True)
+parser_forecast_points.add_argument('interval', required=True)
+
+# Add optional uncertainty parameters
+parser_forecast_points.add_argument('temperature_uncertainty', required=False)
+parser_forecast_points.add_argument('solar_uncertainty', required=False)
+
 # ``results`` interface
 results_var = reqparse.RequestParser(argument_class=CustomArgument)
 results_var.add_argument('point_names', type=list, action='append', required=True)
@@ -200,10 +211,19 @@ def put(self):
         args = parser_forecast_points.parse_args()
         horizon = args['horizon']
         interval = args['interval']
+
+        # Extract optional uncertainty parameters if provided
+        temperature_uncertainty = args.get('temperature_uncertainty', None)
+        solar_uncertainty = args.get('solar_uncertainty', None)
+
         point_names = []
         for point_name in args['point_names']:
             point_names.append(''.join(point_name))
-        status, message, payload = case.get_forecast(point_names, horizon, interval)
+
+        # Modify the get_forecast function call to handle the new parameters
+        status, message, payload = case.get_forecast(point_names, horizon, interval, temperature_uncertainty,
+                                                     solar_uncertainty)
+
         return construct(status, message, payload)
 
 class Scenario(Resource):
@@ -276,3 +296,4 @@ def post(self):
 
 if __name__ == '__main__':
     app.run(host='0.0.0.0')
+
diff --git a/testcase.py b/testcase.py
index 1c23c6457..1110375a8 100644
--- a/testcase.py
+++ b/testcase.py
@@ -90,7 +90,11 @@ def __init__(self, fmupath='models/wrapped.fmu'):
         # Initialize test case
         self.initialize(self.config_json['start_time'], self.config_json['warmup_period'])
         # Set default scenario
+        self.config_json['scenario']['solar_uncertainty']=None #todo
+        self.config_json['scenario']['temperature_uncertainty']=None #todo
+        self.config_json['scenario']['seed'] = None #todo
         self.set_scenario(self.config_json['scenario'])
+        self.uncertainty_params = self.load_uncertainty_params()
 
     def __initilize_data(self):
         '''Initializes objects for simulation data storage.
@@ -827,7 +831,7 @@ def get_forecast_points(self):
 
         return status, message, payload
 
-    def get_forecast(self, point_names, horizon, interval):
+    def get_forecast(self, point_names, horizon, interval, temperature_uncertainty=None, solar_uncertainty=None):
         '''Returns the test case data forecast
 
         Parameters
@@ -856,9 +860,46 @@ def get_forecast(self, point_names, horizon, interval):
 
         '''
 
+
         # Get the forecast
         status = 200
         message = "Queried the forecast data successfully."
+
+        if (temperature_uncertainty!=None and 'TDryBul' not in self.forecast_names) or \
+                (solar_uncertainty!=None and 'HGloHor' not in self.forecast_names):
+            payload = None
+            missing_variables = []
+            if temperature_uncertainty and 'TDryBul' not in self.forecast_names:
+                missing_variables.append('TDryBul for temperature uncertainty')
+            if solar_uncertainty and 'HGloHor' not in self.forecast_names:
+                missing_variables.append('HGloHor for solar uncertainty')
+
+            status = 400
+            message = "Set the forecast uncertainty, but the forecast variables do not include: {}.".format(
+                ', '.join(missing_variables))
+            logging.error(message)
+            return status, message, payload
+
+        if 'temperature_uncertainty' in self.scenario or 'solar_uncertainty' in self.scenario:
+            if (self.scenario.get('temperature_uncertainty') and temperature_uncertainty) or (self.scenario.get('solar_uncertainty') and solar_uncertainty):
+                payload = None
+                status = 400
+
+                message = "The scenario configuration already includes specific uncertainty settings. "
+                if (self.scenario.get('temperature_uncertainty') and temperature_uncertainty):
+                    message += "'temperature_uncertainty' is already set to '{}', further overriding of 'temperature_uncertainty' is not allowed. ".format(
+                        self.scenario['temperature_uncertainty'])
+                if (self.scenario.get('solar_uncertainty') and solar_uncertainty):
+                    message += "'solar_uncertainty' is already set to '{}', further overriding of 'solar_uncertainty' is not allowed. ".format(
+                        self.scenario['solar_uncertainty'])
+                logging.error(message)
+                return status, message, payload
+
+            if self.scenario.get('temperature_uncertainty'):
+                temperature_uncertainty=self.scenario['temperature_uncertainty']
+            if self.scenario.get('solar_uncertainty'):
+                solar_uncertainty=self.scenario['solar_uncertainty']
+
         # Check inputs
         try:
             horizon = float(horizon)
@@ -876,18 +917,44 @@ def get_forecast(self, point_names, horizon, interval):
             message = "Invalid value {} for parameter interval. Value must be a float, integer, or string able to be converted to a float, but is {}.".format(interval, type(interval))
             logging.error(message)
             return status, message, payload
-        if horizon < 0:
+
+        if interval <= 0:
             payload = None
             status = 400
-            message = "Invalid value {} for parameter horizon. Value must not be negative.".format(horizon)
+            message = "Invalid value {} for parameter interval. Value must be positive.".format(interval)
             logging.error(message)
             return status, message, payload
-        if interval <= 0:
+            # Check the temperature_uncertainty value
+        # if temperature_uncertainty and 'TDryBul' not in point_names:
+        #     payload = None
+        #     status = 400
+        #     message = "Temperature uncertainty provided but 'TDryBul' is missing in point_names."
+        #     logging.error(message)
+        #     return status, message, payload
+
+        # if solar_uncertainty and 'HGloHor' not in point_names:
+        #     payload = None
+        #     status = 400
+        #     message = "Solar uncertainty provided but 'HGloHor' is missing in point_names."
+        #     logging.error(message)
+        #     return status, message, payload
+        allowed_uncertainties = [None, 'low', 'medium', 'high']
+        if temperature_uncertainty not in allowed_uncertainties:
             payload = None
             status = 400
-            message = "Invalid value {} for parameter interval. Value must be positive.".format(interval)
+            message = "Invalid value for temperature_uncertainty. Allowed values are: {}".format(
+                allowed_uncertainties[1:])
             logging.error(message)
             return status, message, payload
+
+        # Check the solar_uncertainty value
+        if solar_uncertainty not in allowed_uncertainties:
+            payload = None
+            status = 400
+            message = "Invalid value for solar_uncertainty. Allowed values are: {}".format(allowed_uncertainties[1:])
+            logging.error(message)
+            return status, message, payload
+
         wrong_points = []
         for point in point_names:
             if point not in self.forecast_names:
@@ -898,10 +965,36 @@ def get_forecast(self, point_names, horizon, interval):
             message = "Invalid point name(s) {} in parameter point_names.  Check list of available forecast points.".format(wrong_points)
             logging.error(message)
             return status, message, payload
+        # Check for missing point_names related to uncertainty parameters
+
+        temperature_params = {
+            "F0": 0, "K0": 0, "F": 0, "K": 0, "mu": 0
+        }
+
+        solar_params = {
+            "ag0": 0, "bg0": 0, "phi": 0, "ag": 0, "bg": 0
+        }
+
+        if temperature_uncertainty is not None:
+            temperature_params.update(self.uncertainty_params['temperature'][temperature_uncertainty])
+
+        if solar_uncertainty is not None:
+            solar_params.update(self.uncertainty_params['solar'][solar_uncertainty])
+
         try:
-            payload = self.forecaster.get_forecast(point_names,
-                                                   horizon=horizon,
-                                                   interval=interval)
+            if self.scenario['seed'] is not None :
+                applied_seed=int(self.scenario['seed']+self.start_time)
+            else:
+                applied_seed=None
+            payload = self.forecaster.get_forecast(
+                point_names,
+                horizon=horizon,
+                interval=interval,
+                weather_temperature_dry_bulb=temperature_params,
+                weather_solar_global_horizontal=solar_params,
+                seed=applied_seed
+            )
+
         except:
             status = 500
             message = "Failed to query the test case forecast data: {}".format(traceback.format_exc())
@@ -918,7 +1011,9 @@ def set_scenario(self, scenario):
         ----------
         scenario : dict
             {'electricity_price': <'constant' or 'dynamic' or 'highly_dynamic'>,
-             'time_period': see available <str> keys for test case
+             'time_period': see available <str> keys for test case,
+             'temperature_uncertainty':<'low' or 'medium' or 'high'>,
+             'solar_uncertainty':<'low' or 'medium' or 'high'>
             }
             If any value is None, it will not change existing.
 
@@ -933,7 +1028,9 @@ def set_scenario(self, scenario):
             Includes the detailed debug information
         payload: dict
             {'electricity_price': if succeeded in changing then True, else None,
-             'time_period': if succeeded then initial measurements, else None
+             'time_period': if succeeded then initial measurements, else None,
+             'temperature_uncertainty': if succeeded in changing then True, else None,
+             'solar_uncertainty': if succeeded in changing then True, else None
             }
         '''
 
@@ -941,11 +1038,32 @@ def set_scenario(self, scenario):
         message = "Test case scenario was set successfully."
         payload = {
             'electricity_price': None,
-            'time_period': None
+            'time_period': None,
+            'temperature_uncertainty': None,
+            'solar_uncertainty': None,
+            'seed':None,
         }
+
+
         if not hasattr(self, 'scenario'):
             self.scenario = {}
         try:
+
+            if (scenario['temperature_uncertainty'] and 'TDryBul' not in self.forecast_names) or \
+                    (scenario['solar_uncertainty'] and 'HGloHor' not in self.forecast_names):
+
+                missing_variables = []
+                if scenario['temperature_uncertainty'] and 'TDryBul' not in self.forecast_names:
+                    missing_variables.append('TDryBul for temperature uncertainty')
+                if scenario['solar_uncertainty'] and 'HGloHor' not in self.forecast_names:
+                    missing_variables.append('HGloHor for solar uncertainty')
+
+                status = 400
+                message = "Scenario parameters are set for uncertainty, but the forecast variables do not include: {}.".format(
+                    ', '.join(missing_variables))
+                logging.error(message)
+                return status, message, payload
+
             # Handle electricity price
             if scenario['electricity_price']:
                 if scenario['electricity_price'] not in ['constant', 'dynamic', 'highly_dynamic']:
@@ -971,6 +1089,51 @@ def set_scenario(self, scenario):
                 key = self.scenario['time_period']
                 start_time = self.days_json[key]*24*3600.-7*24*3600.
                 end_time = start_time + 14*24*3600.
+
+            # Handle temperature uncertainty
+
+
+
+            if scenario['temperature_uncertainty']:
+                if scenario['temperature_uncertainty'] not in ['low', 'medium', 'high']:
+                    status = 400
+                    message = "Scenario parameter temperature_uncertainty is {}, " \
+                              "but should be 'low', 'medium' or 'high'.". \
+                              format(scenario['temperature_uncertainty'])
+                    logging.error(message)
+                    return status, message, payload
+                self.scenario['temperature_uncertainty'] = scenario['temperature_uncertainty']
+                payload['temperature_uncertainty'] = self.scenario['temperature_uncertainty']
+            else:
+                self.scenario['temperature_uncertainty'] = None
+
+            # Handle solar uncertainty
+            if scenario['solar_uncertainty']:
+                if scenario['solar_uncertainty'] not in ['low', 'medium', 'high']:
+                    status = 400
+                    message = "Scenario parameter solar_uncertainty is {}, " \
+                              "but should be 'low', 'medium' or 'high'.". \
+                        format(scenario['solar_uncertainty'])
+                    logging.error(message)
+                    return status, message, payload
+                self.scenario['solar_uncertainty'] = scenario['solar_uncertainty']
+                payload['solar_uncertainty'] = self.scenario['solar_uncertainty']
+            else:
+                self.scenario['solar_uncertainty'] = None
+
+            if scenario['seed']:
+                if isinstance(scenario['seed'], int) and scenario['seed'] >= 0:
+                    self.scenario['seed'] = scenario['seed']
+                    payload['seed'] = self.scenario['seed']
+                else:
+                    status = 400
+                    message = "Scenario parameter seed is {}, " \
+                              "but should be a non-negative integer.".format(scenario['seed'])
+                    logging.error(message)
+                    return status, message, payload
+            else:
+                self.scenario['seed'] = None
+
         except:
             status = 400
             message = "Invalid values for the scenario parameters: {}".format(traceback.format_exc())
@@ -1392,3 +1555,21 @@ def add_forecast_uncertainty(self, scenario):
             scenario['weatherForecastUncertainty'] = 'deterministic'
 
         return scenario
+
+    def load_uncertainty_params(self, filepath='forecast/forecast_uncertainty_params.json'):
+        '''Load the uncertainty parameters from a JSON file.
+
+        Parameters
+        ----------
+        filepath : str, optional
+            Path to the JSON file containing the uncertainty parameters.
+            Default is 'forecast_uncertainty_params.json'.
+
+        Returns
+        -------
+        dict
+            Uncertainty parameters loaded from the JSON file.
+        '''
+        with open(filepath, 'r') as f:
+            uncertainty_params = json.load(f)
+        return uncertainty_params
\ No newline at end of file
diff --git a/testcases/bestest_air/models/config.json b/testcases/bestest_air/models/config.json
index 6f11cb31e..c9967d19a 100644
--- a/testcases/bestest_air/models/config.json
+++ b/testcases/bestest_air/models/config.json
@@ -1,9 +1,11 @@
 {
-"name"          : "bestest_air",
-"area"          : 48.0,
-"start_time"    : 0.0,
-"warmup_period" : 0.0,
-"step"          : 3600.0,
-"scenario"      : {"electricity_price":"constant",
-                   "time_period":null}
+  "name": "bestest_air",
+  "area": 48.0,
+  "start_time": 0.0,
+  "warmup_period": 0.0,
+  "step": 3600.0,
+  "scenario": {
+    "electricity_price": "constant",
+    "time_period": null
+  }
 }
diff --git a/testcases/bestest_hydronic/models/config.json b/testcases/bestest_hydronic/models/config.json
index e8e2743a1..6003cefb8 100644
--- a/testcases/bestest_hydronic/models/config.json
+++ b/testcases/bestest_hydronic/models/config.json
@@ -5,5 +5,6 @@
 "warmup_period" : 0.0,
 "step"          : 3600.0,
 "scenario"      : {"electricity_price":"constant",
-                   "time_period":null}
+                   "time_period":null,
+                    "weather_forecast_uncertainty": null}
 }
diff --git a/testcases/bestest_hydronic_heat_pump/models/config.json b/testcases/bestest_hydronic_heat_pump/models/config.json
index 4844299ce..d12e584f5 100644
--- a/testcases/bestest_hydronic_heat_pump/models/config.json
+++ b/testcases/bestest_hydronic_heat_pump/models/config.json
@@ -5,5 +5,6 @@
 "warmup_period" : 0.0,
 "step"          : 3600.0,
 "scenario"      : {"electricity_price":"constant",
-                   "time_period":null}
+                   "time_period":null,
+                    "weather_forecast_uncertainty": null}
 }
diff --git a/testcases/multizone_office_simple_air/models/config.json b/testcases/multizone_office_simple_air/models/config.json
index 2572e5d93..b3ee5dfcd 100644
--- a/testcases/multizone_office_simple_air/models/config.json
+++ b/testcases/multizone_office_simple_air/models/config.json
@@ -5,5 +5,6 @@
 "warmup_period" : 0.0,
 "step"          : 3600.0,
 "scenario"      : {"electricity_price":"constant",
-                   "time_period":null}
+                   "time_period":null,
+                    "weather_forecast_uncertainty": null}
 }
diff --git a/testcases/multizone_residential_hydronic/models/config.json b/testcases/multizone_residential_hydronic/models/config.json
index 9ffe0920d..a5750e10f 100644
--- a/testcases/multizone_residential_hydronic/models/config.json
+++ b/testcases/multizone_residential_hydronic/models/config.json
@@ -5,5 +5,6 @@
 "warmup_period" : 0.0,
 "step"          : 3600.0,
 "scenario"      : {"electricity_price":"constant",
-                   "time_period":null}
+                   "time_period":null,
+                    "weather_forecast_uncertainty": null}
 }
diff --git a/testcases/singlezone_commercial_hydronic/models/config.json b/testcases/singlezone_commercial_hydronic/models/config.json
index d776a82d6..b192d299c 100644
--- a/testcases/singlezone_commercial_hydronic/models/config.json
+++ b/testcases/singlezone_commercial_hydronic/models/config.json
@@ -5,5 +5,6 @@
 "warmup_period" : 0.0,
 "step"          : 3600.0,
 "scenario"      : {"electricity_price":"constant",
-                   "time_period":null}
+                   "time_period":null,
+                    "weather_forecast_uncertainty": null}
 }
diff --git a/testcases/testcase1/models/config.json b/testcases/testcase1/models/config.json
index 64e615525..c8f4944e7 100644
--- a/testcases/testcase1/models/config.json
+++ b/testcases/testcase1/models/config.json
@@ -4,4 +4,5 @@
 "warmup_period" : 0.0,
 "step"          : 60.0,
 "scenario"      : {"electricity_price":"constant",
-                   "time_period":null}}
+                   "time_period":null,
+                    "weather_forecast_uncertainty": null}}
diff --git a/testcases/testcase2/models/config.json b/testcases/testcase2/models/config.json
index edd69f4ad..bb46ec45d 100644
--- a/testcases/testcase2/models/config.json
+++ b/testcases/testcase2/models/config.json
@@ -5,5 +5,6 @@
 "warmup_period" : 0.0,
 "step"          : 3600.0,
 "scenario"      : {"electricity_price":"constant",
-                   "time_period":null}
+                   "time_period":null,
+                    "weather_forecast_uncertainty": null}
 }
diff --git a/testcases/testcase3/models/config.json b/testcases/testcase3/models/config.json
index aecf6723b..6017e594c 100644
--- a/testcases/testcase3/models/config.json
+++ b/testcases/testcase3/models/config.json
@@ -5,5 +5,6 @@
 "warmup_period" : 0.0,
 "step"          : 60.0,
 "scenario"      : {"electricity_price":"constant",
-                   "time_period":null}
+                   "time_period":null,
+                    "weather_forecast_uncertainty": null}
 }
diff --git a/testcases/twozone_apartment_hydronic/models/config.json b/testcases/twozone_apartment_hydronic/models/config.json
index 3fee380a0..6e8384731 100644
--- a/testcases/twozone_apartment_hydronic/models/config.json
+++ b/testcases/twozone_apartment_hydronic/models/config.json
@@ -6,4 +6,5 @@
 "horizon"       : 86400.0,
 "interval"      : 3600.0,
 "scenario"      : {"electricity_price":"constant",
-                   "time_period":null}}
+                   "time_period":null,
+                    "weather_forecast_uncertainty": null}}
diff --git a/testing/references/forecast/forecast_uncertainty_params.json b/testing/references/forecast/forecast_uncertainty_params.json
new file mode 100644
index 000000000..f2b99db71
--- /dev/null
+++ b/testing/references/forecast/forecast_uncertainty_params.json
@@ -0,0 +1,49 @@
+{
+    "temperature": {
+        "low": {
+            "F0": 0,
+            "K0": 0.6,
+            "F": 0.92,
+            "K": 0.4,
+            "mu": 0
+        },
+        "medium": {
+            "F0": 0.15,
+            "K0": 1.2,
+            "F": 0.93,
+            "K": 0.6,
+            "mu": 0
+        },
+        "high": {
+            "F0": -0.58,
+            "K0": 1.5,
+            "F": 0.95,
+            "K": 0.7,
+            "mu": -0.015
+        }
+    },
+    "solar": {
+        "low": {
+            "ag0": 4.44,
+            "bg0": 57.42,
+            "phi": 0.62,
+            "ag": 1.86,
+            "bg": 45.64
+        },
+        "medium": {
+            "ag0": 15.02,
+            "bg0": 122.6,
+            "phi": 0.63,
+            "ag": 4.44,
+            "bg": 91.97
+        },
+        "high": {
+            "ag0": 32.09,
+            "bg0": 119.94,
+            "phi": 0.67,
+            "ag": 10.63,
+            "bg": 87.44
+        }
+    }
+
+}
diff --git a/testing/references/forecast/testcase2/determinstic_solar_forecasts.csv b/testing/references/forecast/testcase2/determinstic_solar_forecasts.csv
new file mode 100644
index 000000000..78681d03a
--- /dev/null
+++ b/testing/references/forecast/testcase2/determinstic_solar_forecasts.csv
@@ -0,0 +1,1001 @@
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diff --git a/testing/references/forecast/testcase2/determinstic_temperature_forecasts.csv b/testing/references/forecast/testcase2/determinstic_temperature_forecasts.csv
new file mode 100644
index 000000000..921ec2d33
--- /dev/null
+++ b/testing/references/forecast/testcase2/determinstic_temperature_forecasts.csv
@@ -0,0 +1,1001 @@
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diff --git a/testing/references/forecast/testcase3/determinstic_temperature_forecasts.csv b/testing/references/forecast/testcase3/determinstic_temperature_forecasts.csv
new file mode 100644
index 000000000..c60b34333
--- /dev/null
+++ b/testing/references/forecast/testcase3/determinstic_temperature_forecasts.csv
@@ -0,0 +1,1001 @@
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diff --git a/testing/test_forecast_uncertainty_MultiZone.py b/testing/test_forecast_uncertainty_MultiZone.py
new file mode 100644
index 000000000..566cdae89
--- /dev/null
+++ b/testing/test_forecast_uncertainty_MultiZone.py
@@ -0,0 +1,174 @@
+# -*- coding: utf-8 -*-
+"""
+This module runs tests for testcase3. To run these tests, testcase3 must already be deployed.Ensure 'testcase3'
+is deployed by running `TESTCASE=testcase3 docker-compose up` in the terminal at the root directory of the software.
+It includes tests to check forecast intervals, horizon, and uncertainty levels.
+
+"""
+
+import os
+import unittest
+import numpy as np
+import pandas as pd
+import json
+import requests
+import utilities
+from forecast.forecaster import Forecaster
+
+testing_root_dir = os.path.join(utilities.get_root_path(), 'testing')
+
+
+
+class ForecasterSingleZoneTest(unittest.TestCase):
+
+
+    def setUp(self):
+        '''Setup for each test.
+
+        Changes the directory to testcase2 and initializes test case attributes.
+
+        '''
+
+        # Change directory to testcase 3
+        os.chdir(os.path.join(testing_root_dir,'testcase3'))
+        from testcase3.testcase import TestCase
+        self.case=TestCase()
+
+        # Instantiate a forecaster
+        self.forecaster = Forecaster(self.case)
+
+        self.forecast_points = list(self.case.get_forecast_points()[2].keys())
+        # Load deterministic forecasts
+        self.determinstic_temperature_forecasts=pd.read_csv(os.path.join(utilities.get_root_path(),
+            'testing', 'references', 'forecast', 'testcase3', 'determinstic_temperature_forecasts.csv'),index_col=0)
+
+        # Load uncertainty parameters
+        with open(os.path.join(utilities.get_root_path(),
+            'testing', 'references', 'forecast','forecast_uncertainty_params.json'), 'r') as f:
+            uncertainty_params = json.load(f)
+        self.ref_temperature_uncertainty_params=uncertainty_params['temperature']
+
+        # Set URL for testcase
+        self.url = 'http://127.0.0.1:5000'
+        self.input_names=self.case.input_names
+        self.inputs_metadata=self.case.inputs_metadata
+
+    def test_interval_horizon(self):
+        """Test whether forecast intervals and horizon are correct."""
+        uncertain_level = 'low'
+        requests.put('{0}/scenario'.format(self.url), json={
+            'electricity_price': 'constant',
+            'temperature_uncertainty': uncertain_level,
+            'seed': 5
+        })
+        forecasts = requests.put('{0}/forecast'.format(self.url),
+                                 json={'point_names': ['TDryBul'],
+                                       'interval': 3600,
+                                       'horizon': 48 * 3600},
+                                 ).json()['payload']
+        self.assertEqual(len(forecasts['TDryBul']), 49)
+
+        # Verify interval is consistent and equals 3600 seconds
+        time_data=forecasts['time']
+        for  i in range(1,len(time_data)):
+            interval = time_data[i] - time_data[i - 1]
+            self.assertEqual(interval, 3600)
+
+    def test_low_uncertainty(self):
+        """Test forecasts under low uncertainty."""
+        self.check_uncertainty(uncertain_level='low')
+
+    def test_medium_uncertainty(self):
+        """Test forecasts under medium uncertainty."""
+        self.check_uncertainty(uncertain_level='medium')
+
+    def test_high_uncertainty(self):
+        """Test forecasts under high uncertainty."""
+        self.check_uncertainty(uncertain_level='high')
+
+    def test_forecast_temperature_are_within_range(self):
+        requests.put('{0}/scenario'.format(self.url), json={
+            'electricity_price': 'constant',
+            'temperature_uncertainty': 'high',
+            'seed': 5
+        })
+        u = {
+            self.input_names[0]: float(1),
+            self.input_names[1]: float(self.inputs_metadata[self.input_names[1]]['Minimum'])
+        }
+
+        for _ in range(1000):
+
+            forecasts=requests.put('{0}/forecast'.format(self.url),
+                         json={'point_names': ['TDryBul'],
+                               'interval': 3600,
+                               'horizon': 48 * 3600},
+                         ).json()['payload']['TDryBul']
+            for forecast in forecasts:
+                self.assertGreaterEqual(forecast, 173.15, f"Forecast temperature {forecast} is below -100°C")
+                self.assertLessEqual(forecast, 373.15, f"Forecast temperature {forecast} is above 100°C")
+            requests.post('{0}/advance'.format(self.url), json=u).json()
+
+
+    def check_uncertainty(self,uncertain_level):
+        """Check the forecast uncertainty parameters against references.
+
+        Parameters
+        ----------
+        uncertain_level : str
+        The level of uncertainty to test ('low', 'medium', 'high').
+
+        This method compares the forecast uncertainty parameters with the reference values for a given uncertainty level.
+
+        """
+        # Extract reference uncertainty parameters
+        ref_F0, ref_K0, ref_F, ref_K, ref_mu=self.ref_temperature_uncertainty_params[uncertain_level].values()
+
+        # Initialize and set scenario
+        requests.put('{0}/initialize'.format(self.url),
+                     json={'start_time': int(0),
+                           'warmup_period': int(0)})
+        requests.put('{0}/step'.format(self.url), json={'step': int(3600)})
+
+        requests.put('{0}/scenario'.format(self.url), json={
+            'electricity_price': 'constant',
+            'temperature_uncertainty': uncertain_level,
+            'seed': 5
+        })
+
+        # Collect forecasts and calculate errors
+        all_temperature_forecasts = []
+        u={
+            self.input_names[0]:float(1),
+            self.input_names[1]:float(self.inputs_metadata[self.input_names[1]]['Minimum'])
+        }
+        for _ in range(1000):
+
+            forecasts=requests.put('{0}/forecast'.format(self.url),
+                         json={'point_names': ['TDryBul'],
+                               'interval': 3600,
+                               'horizon': 48 * 3600},
+                         ).json()['payload']
+            current_temperature_forecast=forecasts['TDryBul']
+            all_temperature_forecasts.append(current_temperature_forecast)
+
+            requests.post('{0}/advance'.format(self.url), json=u).json()
+
+
+        # Calculate error between deterministic and forecasted data
+        temperature_error=self.determinstic_temperature_forecasts.values-np.array(all_temperature_forecasts)
+
+        # Check parameters
+        F0, K0, F, K, mu=utilities.check_params_gaussain(temperature_error)
+
+        # Assert the forecast uncertainty parameters are within acceptable ranges
+        self.assertAlmostEqual(F0, ref_F0, delta=0.2)
+        self.assertAlmostEqual(K0, ref_K0, delta=0.2)
+        self.assertAlmostEqual(F, ref_F, delta=0.01)
+        self.assertAlmostEqual(K, ref_K, delta=0.01)
+        self.assertAlmostEqual(mu, ref_mu, delta=0.01)
+
+
+if __name__ == '__main__':
+
+    utilities.run_tests(os.path.basename(__file__))
diff --git a/testing/test_forecast_uncertainty_SingleZone.py b/testing/test_forecast_uncertainty_SingleZone.py
new file mode 100644
index 000000000..d562e9713
--- /dev/null
+++ b/testing/test_forecast_uncertainty_SingleZone.py
@@ -0,0 +1,204 @@
+# -*- coding: utf-8 -*-
+"""
+This module runs tests for testcase2. To run these tests, testcase2 must already be deployed.Ensure 'testcase2'
+is deployed by running `TESTCASE=testcase2 docker-compose up` in the terminal at the root directory of the software.
+It includes tests to check forecast intervals, horizon, and uncertainty levels.
+
+"""
+
+import os
+import unittest
+import numpy as np
+import pandas as pd
+import json
+import requests
+import utilities
+from forecast.forecaster import Forecaster
+
+testing_root_dir = os.path.join(utilities.get_root_path(), 'testing')
+
+
+
+class ForecasterSingleZoneTest(unittest.TestCase):
+
+
+    def setUp(self):
+        '''Setup for each test.
+
+        Changes the directory to testcase2 and initializes test case attributes.
+
+        '''
+
+        # Change directory to testcase 2
+        os.chdir(os.path.join(testing_root_dir,'testcase2'))
+        from testcase2.testcase import TestCase
+        self.case=TestCase()
+
+        # Instantiate a forecaster
+        self.forecaster = Forecaster(self.case)
+
+        # Load deterministic forecasts
+        self.determinstic_temperature_forecasts=pd.read_csv(os.path.join(utilities.get_root_path(),
+            'testing', 'references', 'forecast', 'testcase2', 'determinstic_temperature_forecasts.csv'),index_col=0)
+        self.determinstic_solar_forecasts=pd.read_csv(os.path.join(utilities.get_root_path(),
+            'testing', 'references', 'forecast', 'testcase2', 'determinstic_solar_forecasts.csv'),index_col=0)
+
+        # Load uncertainty parameters
+        with open(os.path.join(utilities.get_root_path(),
+            'testing', 'references', 'forecast','forecast_uncertainty_params.json'), 'r') as f:
+            uncertainty_params = json.load(f)
+        self.ref_temperature_uncertainty_params=uncertainty_params['temperature']
+        self.ref_solar_uncertainty_params = uncertainty_params['solar']
+
+        # Set URL for testcase
+        self.url = 'http://127.0.0.1:5000'
+        self.input_names=self.case.input_names
+        self.inputs_metadata=self.case.inputs_metadata
+
+    def test_interval_horizon(self):
+        """Test whether forecast intervals and horizon are correct."""
+        uncertain_level = 'low'
+        requests.put('{0}/scenario'.format(self.url), json={
+            'electricity_price': 'constant',
+            'solar_uncertainty': uncertain_level,
+            'temperature_uncertainty': uncertain_level,
+            'seed':5
+        })
+        forecasts = requests.put('{0}/forecast'.format(self.url),
+                                 json={'point_names': ['TDryBul', 'HGloHor'],
+                                       'interval': 3600,
+                                       'horizon': 48 * 3600},
+                                 ).json()['payload']
+        self.assertEqual(len(forecasts['TDryBul']), 49)
+
+        # Verify interval is consistent and equals 3600 seconds
+        time_data=forecasts['time']
+        for  i in range(1,len(time_data)):
+            interval = time_data[i] - time_data[i - 1]
+            self.assertEqual(interval, 3600)
+
+    def test_low_uncertainty(self):
+        """Test forecasts under low uncertainty."""
+        self.check_uncertainty(uncertain_level='low')
+    #
+    def test_medium_uncertainty(self):
+        """Test forecasts under medium uncertainty."""
+        self.check_uncertainty(uncertain_level='medium')
+
+    def test_high_uncertainty(self):
+        """Test forecasts under high uncertainty."""
+        self.check_uncertainty(uncertain_level='high')
+
+    def test_forecast_solar_radiation_are_positive(self):
+        requests.put('{0}/scenario'.format(self.url), json={
+            'electricity_price': 'constant',
+            'solar_uncertainty': 'high',
+            'seed': 5
+        })
+        u = {
+            self.input_names[0]: float(1),
+            self.input_names[1]: float(self.inputs_metadata[self.input_names[1]]['Minimum'])
+        }
+
+        for _ in range(1000):
+
+            forecasts=requests.put('{0}/forecast'.format(self.url),
+                         json={'point_names': ['HGloHor'],
+                               'interval': 3600,
+                               'horizon': 48 * 3600},
+                         ).json()['payload']['HGloHor']
+            for forecast in forecasts:
+                self.assertGreaterEqual(forecast, 0, f"Forecast value {forecast} is not greater than 0")
+            requests.post('{0}/advance'.format(self.url), json=u).json()
+
+    def test_forecast_temperature_are_within_range(self):
+        requests.put('{0}/scenario'.format(self.url), json={
+            'electricity_price': 'constant',
+            'temperature_uncertainty': 'high',
+            'seed': 5
+        })
+        u = {
+            self.input_names[0]: float(1),
+            self.input_names[1]: float(self.inputs_metadata[self.input_names[1]]['Minimum'])
+        }
+
+        for _ in range(1000):
+
+            forecasts=requests.put('{0}/forecast'.format(self.url),
+                         json={'point_names': ['TDryBul'],
+                               'interval': 3600,
+                               'horizon': 48 * 3600},
+                         ).json()['payload']['TDryBul']
+            for forecast in forecasts:
+                self.assertGreaterEqual(forecast, 173.15, f"Forecast temperature {forecast} is below -100°C")
+                self.assertLessEqual(forecast, 373.15, f"Forecast temperature {forecast} is above 100°C")
+            requests.post('{0}/advance'.format(self.url), json=u).json()
+
+    def check_uncertainty(self,uncertain_level):
+        """Check the forecast uncertainty parameters against references.
+
+        Parameters
+        ----------
+        uncertain_level : str
+        The level of uncertainty to test ('low', 'medium', 'high').
+
+        This method compares the forecast uncertainty parameters with the reference values for a given uncertainty level.
+
+        """
+        # Extract reference uncertainty parameters
+        ref_F0, ref_K0, ref_F, ref_K, ref_mu=self.ref_temperature_uncertainty_params[uncertain_level].values()
+        ref_ag0, ref_bg0, ref_phi, ref_ag, ref_bg=self.ref_solar_uncertainty_params[uncertain_level].values()
+
+        # Initialize and set scenario
+        requests.put('{0}/initialize'.format(self.url),
+                     json={'start_time': int(0),
+                           'warmup_period': int(0)})
+        requests.put('{0}/step'.format(self.url), json={'step': int(3600)})
+
+        requests.put('{0}/scenario'.format(self.url), json={
+            'electricity_price': 'constant',
+            'solar_uncertainty': uncertain_level,
+            'temperature_uncertainty': uncertain_level,
+            'seed':5
+        })
+
+        # Collect forecasts and calculate errors
+        all_temperature_forecasts = []
+        all_solar_forecasts = []
+        u={
+            self.input_names[0]:float(1),
+            self.input_names[1]:float(self.inputs_metadata[self.input_names[1]]['Minimum'])
+        }
+        for _ in range(1000):
+
+            forecasts=requests.put('{0}/forecast'.format(self.url),
+                         json={'point_names': ['TDryBul', 'HGloHor'],
+                               'interval': 3600,
+                               'horizon': 48 * 3600},
+                         ).json()['payload']
+            current_temperature_forecast=forecasts['TDryBul']
+            current_solar_forecast=forecasts['HGloHor']
+            all_temperature_forecasts.append(current_temperature_forecast)
+            all_solar_forecasts.append(current_solar_forecast)
+
+            requests.post('{0}/advance'.format(self.url), json=u).json()
+
+        # Calculate error between deterministic and forecasted data
+        temperature_error=self.determinstic_temperature_forecasts.values-np.array(all_temperature_forecasts)
+        solar_error=self.determinstic_solar_forecasts.values-np.array(all_solar_forecasts)
+
+        # Check parameters
+        F0, K0, F, K, mu=utilities.check_params_gaussain(temperature_error)
+        ag0, bg0, phi, ag, bg=utilities.check_params_laplace(solar_error)
+
+        # Assert the forecast uncertainty parameters are within acceptable ranges
+        self.assertAlmostEqual(F0, ref_F0, delta=0.2)
+        self.assertAlmostEqual(K0, ref_K0, delta=0.2)
+        self.assertAlmostEqual(F, ref_F, delta=0.01)
+        self.assertAlmostEqual(K, ref_K, delta=0.01)
+        self.assertAlmostEqual(mu, ref_mu, delta=0.01)
+
+
+if __name__ == '__main__':
+
+    utilities.run_tests(os.path.basename(__file__))
diff --git a/testing/testcase2/README.md b/testing/testcase2/README.md
new file mode 100644
index 000000000..8b0eb3329
--- /dev/null
+++ b/testing/testcase2/README.md
@@ -0,0 +1,10 @@
+BOPTEST Test Case 2
+===================
+This test case is a single zone VAV system.
+
+Structure
+---------
+
+- ``doc/`` contains documentation of the test case.
+- ``models/`` contains the emulation model files for the test case.
+- ``config.py`` defines the configuration of the test case for building the Docker image.
diff --git a/testing/testcase2/__init__.py b/testing/testcase2/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/testing/testcase2/forecast/README.md b/testing/testcase2/forecast/README.md
new file mode 100644
index 000000000..d6fefe2dd
--- /dev/null
+++ b/testing/testcase2/forecast/README.md
@@ -0,0 +1,13 @@
+This directory contains the Forecaster module to retrieve forecast
+from a BOPTEST test case, which is needed for predictive controllers.
+This module uses the data_manager object of a test case to read the
+deterministic forecast from the test case FMU. In future
+developments it will be possible to request stochastic forecast with
+a predefined distribution over the deterministic forecast for research
+purposes. This distribution will be added on the top of the
+deterministic forecast mentioned before.
+
+The controller developer can choose the prediction horizon and
+interval of the forecast from the actual simulation time. The
+controller developer may also filter the forecast for a specific data
+category or request all data variables and filter it afterwards.
diff --git a/testing/testcase2/forecast/__init__.py b/testing/testcase2/forecast/__init__.py
new file mode 100644
index 000000000..29e4d987e
--- /dev/null
+++ b/testing/testcase2/forecast/__init__.py
@@ -0,0 +1,6 @@
+'''
+Created on Apr 25, 2019
+
+@author: Javier Arroyo
+
+'''
diff --git a/testing/testcase2/forecast/error_emulator.py b/testing/testcase2/forecast/error_emulator.py
new file mode 100644
index 000000000..ec9f100f6
--- /dev/null
+++ b/testing/testcase2/forecast/error_emulator.py
@@ -0,0 +1,86 @@
+'''
+Created on Sept 5, 2022
+
+@author: Laura Zabala
+
+'''
+
+import numpy as np
+
+
+
+def mean_filter(data, window_size=3):
+    """
+    Apply mean filter to a 1D list of data.
+
+    Parameters:
+    - data: List of numbers to be filtered.
+    - window_size: Size of the filtering window. Must be an odd number.
+
+    Returns:
+    - Filtered data.
+    """
+    if window_size % 2 == 0:
+        raise ValueError("Window size must be an odd number.")
+
+    half_window = window_size // 2
+    filtered_data = data.copy()
+
+    for i in range(half_window, len(data) - half_window):
+        if data[i] == 0:
+            continue
+
+        window_data = data[i - half_window : i + half_window + 1]
+        filtered_data[i] = sum(window_data) / len(window_data)
+
+    return filtered_data
+def predict_temperature_error_AR1(hp, F0, K0, F, K, mu):
+    '''Generates an error with an AR model in the hp points of the predictions horizon.
+
+    Parameters
+    ----------
+    hp : int, number of points in the prediction horizon
+    F0 : float, mean of the initial error model
+    K0 : float, standard deviation of the initial error model
+    F : float from 0 to 1, autocorrelation factor of the AR error model
+    K : float, standard deviation of the AR error model
+    mu : float, mean value of the distribution function integrated in the AR error model
+
+    Returns
+    -------
+    error : 1D array with the error values in the hp points
+
+    '''
+    hp=int(hp)
+    error = np.zeros(hp)
+    error[0,] = np.random.normal(F0, K0)
+    for i_c in range(hp - 1):
+        error[i_c + 1] = np.random.normal(
+            error[i_c] * F + mu, K
+        )
+    return error
+
+
+
+def predict_solar_error_AR1(hp, ag0, bg0, phi, ag, bg):
+    '''Generates an error based on the specified parameters using an AR model in the hp points of the predictions horizon.
+
+        Parameters
+        ----------
+        hp : int
+            Number of points in the prediction horizon.
+        ag0, bg0, phi, ag, bg : float
+            Parameters for the AR1 model.
+
+        Returns
+        -------
+        error : 1D numpy array
+            Contains the error values in the hp points.
+    '''
+    hp = int(hp)
+    error = np.zeros(hp)
+    error[0] = np.random.laplace(ag0, bg0)
+    for i_c in range(1, hp):
+        error[i_c] = np.random.laplace(error[i_c - 1] * phi + ag, bg)
+
+    return error
diff --git a/testing/testcase2/forecast/forecast_uncertainty_params.json b/testing/testcase2/forecast/forecast_uncertainty_params.json
new file mode 100644
index 000000000..f2b99db71
--- /dev/null
+++ b/testing/testcase2/forecast/forecast_uncertainty_params.json
@@ -0,0 +1,49 @@
+{
+    "temperature": {
+        "low": {
+            "F0": 0,
+            "K0": 0.6,
+            "F": 0.92,
+            "K": 0.4,
+            "mu": 0
+        },
+        "medium": {
+            "F0": 0.15,
+            "K0": 1.2,
+            "F": 0.93,
+            "K": 0.6,
+            "mu": 0
+        },
+        "high": {
+            "F0": -0.58,
+            "K0": 1.5,
+            "F": 0.95,
+            "K": 0.7,
+            "mu": -0.015
+        }
+    },
+    "solar": {
+        "low": {
+            "ag0": 4.44,
+            "bg0": 57.42,
+            "phi": 0.62,
+            "ag": 1.86,
+            "bg": 45.64
+        },
+        "medium": {
+            "ag0": 15.02,
+            "bg0": 122.6,
+            "phi": 0.63,
+            "ag": 4.44,
+            "bg": 91.97
+        },
+        "high": {
+            "ag0": 32.09,
+            "bg0": 119.94,
+            "phi": 0.67,
+            "ag": 10.63,
+            "bg": 87.44
+        }
+    }
+
+}
diff --git a/testing/testcase2/forecast/forecaster.py b/testing/testcase2/forecast/forecaster.py
new file mode 100644
index 000000000..e0ba1640c
--- /dev/null
+++ b/testing/testcase2/forecast/forecaster.py
@@ -0,0 +1,99 @@
+'''
+Created on Apr 25, 2019
+
+@author: Javier Arroyo
+
+This module contains the Forecaster class with methods to obtain
+forecast data for the test case. It relies on the data_manager object
+of the test case to provide deterministic forecast.
+
+'''
+from .error_emulator import predict_temperature_error_AR1, predict_solar_error_AR1,mean_filter
+import numpy as np
+
+
+class Forecaster(object):
+    '''This class retrieves test case data forecast for its use in
+    optimal control strategies.
+
+    '''
+
+    def __init__(self, testcase):
+        '''
+        Constructor
+
+        Parameters
+        ----------
+        testcase: BOPTEST TestCase object
+            object of an already deployed test case that
+            contains the data stored from the test case run
+
+        '''
+
+        # Point to the test case object
+        self.case = testcase
+
+    def get_forecast(self, point_names, horizon=24 * 3600, interval=3600,
+                     weather_temperature_dry_bulb=None, weather_solar_global_horizontal=None,
+                     category=None, plot=False):
+
+        if weather_temperature_dry_bulb is None:
+            weather_temperature_dry_bulb = {
+                "F0": 0, "K0": 0, "F": 0, "K": 0, "mu": 0
+            }
+
+        if weather_solar_global_horizontal is None:
+            weather_solar_global_horizontal = {
+                "ag0": 0, "bg0": 0, "phi": 0, "ag": 0, "bg": 0
+            }
+        # Get the forecast
+        forecast = self.case.data_manager.get_data(variables=point_names,
+                                                   horizon=horizon,
+                                                   interval=interval,
+                                                   category=category,
+                                                   plot=plot)
+        if 'TDryBul' in point_names and any(weather_temperature_dry_bulb.values()):
+            # error in the forecast
+            error_forecast_temp = predict_temperature_error_AR1(
+                hp=horizon / interval + 1,
+                F0=weather_temperature_dry_bulb["F0"],
+                K0=weather_temperature_dry_bulb["K0"],
+                F=weather_temperature_dry_bulb["F"],
+                K=weather_temperature_dry_bulb["K"],
+                mu=weather_temperature_dry_bulb["mu"]
+            )
+
+            # forecast error just added to dry bulb temperature
+            forecast['TDryBul'] = forecast['TDryBul'] + error_forecast_temp
+            forecast['TDryBul']=forecast['TDryBul'].tolist()
+        if 'HGloHor' in point_names and any(weather_solar_global_horizontal.values()):
+
+            original_HGloHor = np.array(forecast['HGloHor']).copy()
+            lower_bound = 0.2 * original_HGloHor
+            upper_bound = 2 * original_HGloHor
+            indices = np.where(original_HGloHor > 50)[0]
+            for _ in range(200):
+            # while True:
+                error_forecast_solar = predict_solar_error_AR1(
+                    horizon / interval + 1,
+                    weather_solar_global_horizontal["ag0"],
+                    weather_solar_global_horizontal["bg0"],
+                    weather_solar_global_horizontal["phi"],
+                    weather_solar_global_horizontal["ag"],
+                    weather_solar_global_horizontal["bg"]
+                )
+
+                forecast['HGloHor'] = original_HGloHor + error_forecast_solar
+
+                # Check if any point in forecast['HGloHor'] is out of the specified range
+
+                condition = np.any((forecast['HGloHor'][indices] > 2 * original_HGloHor[indices]) |
+                               (forecast['HGloHor'][indices] < 0.2 * original_HGloHor[indices]))
+                # forecast['HGloHor']=gaussian_filter_ignoring_nans(forecast['HGloHor'])
+                forecast['HGloHor'] = mean_filter(forecast['HGloHor'])
+                forecast['HGloHor'] = np.clip(forecast['HGloHor'], lower_bound, upper_bound)
+                forecast['HGloHor']=forecast['HGloHor'].tolist()
+                if not condition:
+                    break
+
+        return forecast
diff --git a/testing/testcase2/models/wrapped.fmu b/testing/testcase2/models/wrapped.fmu
new file mode 100644
index 000000000..ac06c7e28
Binary files /dev/null and b/testing/testcase2/models/wrapped.fmu differ
diff --git a/testing/testcase2/testcase.py b/testing/testcase2/testcase.py
new file mode 100644
index 000000000..8e97f2e57
--- /dev/null
+++ b/testing/testcase2/testcase.py
@@ -0,0 +1,1482 @@
+# -*- coding: utf-8 -*-
+"""
+This module defines the API to the test case used by the REST requests to
+perform functions such as advancing the simulation, retrieving test case
+information, and calculating and reporting results.
+
+"""
+
+from pyfmi import load_fmu
+import numpy as np
+import copy
+import time
+from data.data_manager import Data_Manager
+from forecast.forecaster import Forecaster
+from kpis.kpi_calculator import KPI_Calculator
+import requests
+import traceback
+import logging
+import pytz
+from datetime import datetime
+import uuid
+import os
+import json
+import array as a
+
+class TestCase(object):
+    '''Class that implements the test case.
+
+    '''
+
+    def __init__(self, fmupath='models/wrapped.fmu'):
+        '''Constructor.
+
+        Parameters
+        ----------
+        fmupath : str, optional
+            Path to the test case fmu.
+            Default is assuming a particular directory structure.
+
+        '''
+
+        # Set BOPTEST version number
+        with open('version.txt', 'r') as f:
+            self.version = f.read()
+        # Set test case fmu path and check if path exists and throw execption
+        self.fmupath = fmupath
+        if not os.path.exists(fmupath) or not os.path.isfile(fmupath):
+            raise Exception("The test case FMU cannot be found. Check TESTCASE name entered correctly.")
+        # Instantiate a data manager for this test case
+        self.data_manager = Data_Manager(testcase=self)
+        # Load data and the kpis_json for the test case
+        self.data_manager.load_data_and_jsons()
+        # Instantiate a forecaster for this test case
+        self.forecaster = Forecaster(testcase=self)
+        # Define name
+        self.name = self.config_json['name']
+        # Load fmu
+        self.fmu = load_fmu(self.fmupath)
+        self.fmu.set_log_level(7)
+        # Configure the log, log file, and console output
+        name = 'boptest_{0}'.format(self.name)
+        fmt = '%(asctime)s UTC\t%(name)-20s%(levelname)s\t%(message)s'
+        datefmt = '%m/%d/%Y %I:%M:%S %p'
+        formatter = logging.Formatter(fmt,datefmt)
+        logging.basicConfig(filename='{0}.log'.format(name), filemode='w', level=10, format=fmt, datefmt=datefmt)
+        logger = logging.getLogger()
+        stream_handler = logging.StreamHandler()
+        stream_handler.setFormatter(formatter)
+        logger.addHandler(stream_handler)
+        # Get version and check is 2.0
+        self.fmu_version = self.fmu.get_version()
+        if self.fmu_version != '2.0':
+            raise ValueError('FMU must be version 2.0.')
+        # Get building area
+        self.area = self.config_json['area']
+        # Get available control inputs and outputs
+        self.input_names = list(self.fmu.get_model_variables(causality = 2).keys())
+        self.output_names = list(self.fmu.get_model_variables(causality = 3).keys())
+        self.forecast_names = list(self.data.keys())
+        # Set default communication step
+        self.set_step(self.config_json['step'])
+        # Initialize simulation data arrays
+        self.__initilize_data()
+        # Set default fmu simulation options
+        self.options = self.fmu.simulate_options()
+        self.options['filter'] = self.output_names + self.input_names
+        # Instantiate a KPI calculator for the test case
+        self.cal = KPI_Calculator(testcase=self)
+        # Initialize test case
+        self.initialize(self.config_json['start_time'], self.config_json['warmup_period'])
+        # Set default scenario
+        self.config_json['scenario']['solar_uncertainty']=None #todo
+        self.config_json['scenario']['temperature_uncertainty']=None #todo
+        self.set_scenario(self.config_json['scenario'])
+        self.uncertainty_params = self.load_uncertainty_params()
+
+    def __initilize_data(self):
+        '''Initializes objects for simulation data storage.
+
+        Uses self.output_names and self.input_names to create
+        self.y, self.y_store, self.u, self.u_store,
+        self.inputs_metadata, self.outputs_metadata.
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        None
+
+        '''
+
+        # Get input and output and forecast meta-data
+        self.inputs_metadata = self._get_var_metadata(self.fmu, self.input_names, inputs=True)
+        self.outputs_metadata = self._get_var_metadata(self.fmu, self.output_names)
+        self.forecasts_metadata = self.data_manager.get_data_metadata()
+        # Outputs data
+        self.y = {'time': a.array('d',[])}
+        for key in self.output_names:
+            # Do not store outputs that are current values of control inputs
+            flag = False
+            for key_u in self.input_names:
+                if key[:-2] == key_u[:-2]:
+                    flag = True
+                    break
+            if flag:
+                # Remove outputs that are current values of control inputs
+                # from outputs metadata dictionary
+                self.outputs_metadata.pop(key)
+            else:
+                self.y[key] = a.array('d',[])
+        self.y_store = copy.deepcopy(self.y)
+        # Inputs data
+        self.u = {'time':a.array('d',[])}
+        for key in self.input_names:
+            self.u[key] = a.array('d',[])
+        self.u_store = copy.deepcopy(self.u)
+
+    def __simulation(self,start_time,end_time,input_object=None):
+        '''Simulates the FMU using the pyfmi fmu.simulate function.
+
+        Parameters
+        ----------
+        start_time: int
+            Start time of simulation in seconds.
+        final_time: int
+            Final time of simulation in seconds.
+        input_object: pyfmi input_object, optional
+            Input object for simulation
+            Default is None
+
+        Returns
+        -------
+        res: pyfmi results object
+            Results of the fmu simulation.
+
+        '''
+
+        # Set fmu initialization option
+        self.options['initialize'] = self.initialize_fmu
+        # Set sample rate
+        step = end_time - start_time
+        if step >= 30:
+            self.options['ncp'] = int((end_time-start_time)/30)
+        elif step == 0:
+            pass
+        elif (step < 30) and (step > 0):
+            self.options['ncp'] = int((end_time-start_time)/step)
+        # Simulate fmu
+        try:
+            res = self.fmu.simulate(start_time=start_time,
+                                    final_time=end_time,
+                                    options=self.options,
+                                    input=input_object)
+        except:
+            return traceback.format_exc()
+        # Set internal fmu initialization
+        self.initialize_fmu = False
+
+        return res
+
+    def __get_results(self, res, store=True, store_initial=False):
+        '''Get results at the end of a simulation and throughout the
+        simulation period for storage. This method assigns these results
+        to `self.y` and, if `store=True`, also to `self.y_store` and
+        to `self.u_store`.
+        This method is used by `initialize()` and `advance()` to retrieve
+        results. `initialize()` does not store results whereas `advance()`
+        does.
+
+        Parameters
+        ----------
+        res: pyfmi results object
+            Results of the fmu simulation.
+        store: boolean
+            Set to true if desired to store results in `self.y_store` and
+            `self.u_store`
+        store_initial: boolean
+            Set to true if desired to store the initial point.
+
+        '''
+
+        # Determine if store initial point
+        if store_initial:
+            i = 0
+        else:
+            i = 1
+        # Store measurements
+        for key in self.y.keys():
+            self.y[key] = res[key][-1]
+            if store:
+                # Handle initialization of cs fmu generating multiple points for the same time
+                if res['time'][0] == res['time'][-1]:
+                    self.y_store[key].append(res[key][-1])
+                else:
+                    for x in res[key][i:]:
+                        self.y_store[key].append(x)
+        # Store control signals (will be baseline if not activated, test controller input if activated)
+        for key in self.u.keys():
+            # Replace '_u' and '_y' for key used to collect data and don't overwrite time
+            if key[-2:] == '_u':
+                key_data = key[:-2]+'_y'
+            elif key == 'time':
+                key_data = 'time'
+            else:
+                key_data = key
+            self.u[key] = res[key_data][-1]
+            if store:
+                # Handle initialization of cs fmu generating multiple points for the same time
+                if res['time'][0] == res['time'][-1]:
+                    self.u_store[key].append(res[key_data][-1])
+                else:
+                    for x in res[key_data][i:]:
+                        self.u_store[key].append(x)
+
+    def advance(self, u):
+        '''Advances the test case model simulation forward one step.
+
+        Parameters
+        ----------
+        u : dict
+            Defines the control input data to be used for the step.
+            {<input_name>_activate : bool, int, float, or str convertable to 1 or 0
+             <input_name>_u        : int or float, or str convertable to float}
+
+        Returns
+        -------
+        status: int
+            Indicates whether an advance request has been completed.
+            If 200, simulation advance was completed.
+            If 400, invalid inputs (non-numeric) were identified.
+            If 500, a simulation error occurred.
+        message: str
+            Includes the debug information
+        payload: dict
+            Contains the full state of measurement and input data at the end
+            of the step.
+            {<point_name> : <point_value>}
+            If empty, simulation end time has been reached.
+            If None, a simulation error has occurred.
+        '''
+
+        status = 200
+        # Calculate and store the elapsed time
+        if hasattr(self, 'tic_time'):
+            self.tac_time = time.time()
+            self.elapsed_control_time_ratio = np.append(self.elapsed_control_time_ratio, (self.tac_time-self.tic_time)/self.step)
+
+        # Set final time
+        self.final_time = self.start_time + self.step
+        alert_message = ''
+        # Set control inputs if they exist and are written
+        # Check if possible to overwrite
+        if u.keys():
+            # If there are overwriting keys available
+            # Check that any are overwritten
+            written = False
+            for key in u.keys():
+                if u[key]:
+                    written = True
+                    break
+            # If there are, create input object
+            if written:
+                u_list = []
+                u_trajectory = self.start_time
+                for key in u.keys():
+                    if (key not in self.input_names):
+                        payload = None
+                        status = 400
+                        message = "Unexpected input variable: {}.".format(key)
+                        logging.error(message)
+                        return status, message, payload
+                    if (key != 'time' and (u[key] != None)):
+                        if '_activate' in key:
+                            try:
+                                if float(u[key]) == 1:
+                                    checked_value = 1
+                                elif  float(u[key]) == 0:
+                                    checked_value = 0
+                                else:
+                                    payload = None
+                                    status = 400
+                                    message = "Invalid value {} and/or type {} for input {}. Input must be a boolean, float, integer, string, or unicode able to be converted to a 1 or 0 (or 'T[t]rue' or 'F[f]alse').".format(u[key], type(u[key]), key)
+                                    logging.error(message)
+                                    return status, message, payload
+                            except ValueError:
+                                if (u[key] == 'True') or (u[key] == 'true'):
+                                    checked_value = 1
+                                elif  (u[key] == 'False') or (u[key] == 'false'):
+                                    checked_value = 0
+                                else:
+                                    payload = None
+                                    status = 400
+                                    message = "Invalid value {} and/or type {} for input {}. Input must be a boolean, float, integer, string, or unicode able to be converted to a 1 or 0 (or 'T[t]rue' or 'F[f]alse').".format(u[key], type(u[key]), key)
+                                    logging.error(message)
+                                    return status, message, payload
+                        else:
+                            try:
+                                value = float(u[key])
+                            except:
+                                payload = None
+                                status = 400
+                                message = "Invalid value {} for input {}. Value must be a float, integer, or string able to be converted to a float, but is {}.".format(u[key], key, type(u[key]))
+                                logging.error(message)
+                                return status, message, payload
+                            # Check min/max if not activation input
+                            checked_value, message = self._check_value_min_max(key, value)
+                            if message is not None:
+                                logging.warning(message)
+                                alert_message = message + ';' + alert_message
+                        u_list.append(key)
+                        u_trajectory = np.vstack((u_trajectory, checked_value))
+                input_object = (u_list, np.transpose(u_trajectory))
+            # Otherwise, input object is None
+            else:
+                input_object = None
+        # Otherwise, input object is None
+        else:
+            input_object = None
+        # Simulate if not end of test
+        if self.start_time < self.end_time:
+            # Make sure stop at end of test
+            if self.final_time > self.end_time:
+                self.final_time = self.end_time
+            res = self.__simulation(self.start_time, self.final_time, input_object)
+            # Process results
+            if not isinstance(res, str):
+                # Get result and store measurement and control inputs
+                self.__get_results(res, store=True, store_initial=False)
+                # Raise the flag to compute time lapse
+                self.tic_time = time.time()
+                # Get full current state
+                payload = self._get_full_current_state()
+                # Write any messages
+                if alert_message == '':
+                    message = "Advanced simulation successfully from {0}s to {1}s.".format(self.start_time, self.final_time)
+                else:
+                    message = alert_message
+                # Advance start time
+                self.start_time = self.final_time
+                # Check if scenario is over
+                if self.start_time >= self.end_time:
+                    self.scenario_end = True
+                # Log and return
+                logging.info(message)
+                return status, message, payload
+
+            else:
+                # Errors in the simulation
+                status = 500
+                message = "Failed to advance simulation: {}.".format(res)
+                payload = res
+                logging.error(message)
+                return status, message, payload
+        else:
+            # Simulation at end time
+            payload = dict()
+            message = "End of test case scenario time period reached."
+            logging.info(message)
+
+            return status, message, payload
+
+    def initialize(self, start_time, warmup_period, end_time=np.inf):
+        '''Initialize the test simulation.
+
+        Parameters
+        ----------
+        start_time: int or float
+            Start time of simulation to initialize to in seconds.
+        warmup_period: int or float
+            Length of time before start_time to simulate for warmup in seconds.
+        end_time: int or float, optional
+            Specifies a finite end time to allow the simulation to continue
+            Default value is infinite.
+
+        Returns
+        -------
+        status: int
+            Indicates whether an initialization request has been completed.
+            If 200, initialization was completed successfully.
+            If 400, an invalid start time or warmup period (non-numeric) was identified.
+            If 500, an error occurred during the initialization simulation.
+        message: str
+            Includes detailed debugging information.
+        payload: dict
+            Contains the full state of measurement and input data at the end
+            of the initialization period.
+            {<point_name> : <point_value>}.
+            If None, an error occurred during the initialization simulation.
+
+        '''
+
+        status = 200
+        payload = None
+        # Reset fmu
+        self.fmu.reset()
+        # Reset simulation data storage
+        self.__initilize_data()
+        self.elapsed_control_time_ratio = np.array([])
+        # Check if the inputs are valid
+        try:
+            start_time = float(start_time)
+        except:
+            payload = None
+            status = 400
+            message = "Invalid value {} for parameter start_time. Value must be a float, integer, or string able to be converted to a float, but is {}.".format(start_time, type(start_time))
+            logging.error(message)
+            return status, message, payload
+        try:
+            warmup_period = float(warmup_period)
+        except:
+            payload = None
+            status = 400
+            message = "Invalid value {} for parameter warmup_period. Value must be a float, integer, or string able to be converted to a float, but is {}.".format(warmup_period, type(warmup_period))
+            logging.error(message)
+            return status, message, payload
+        if start_time < 0:
+            payload = None
+            status = 400
+            message = "Invalid value {} for parameter start_time. Value must not be negative.".format(start_time)
+            logging.error(message)
+            return status, message, payload
+        if warmup_period < 0:
+            payload = None
+            status = 400
+            message = "Invalid value {} for parameter warmup_period. Value must not be negative.".format(warmup_period)
+            logging.error(message)
+            return status, message, payload
+        # Record initial testing time
+        self.initial_time = start_time
+        # Record end testing time
+        self.end_time = end_time
+        # Set fmu intitialization
+        self.initialize_fmu = True
+        # Simulate fmu for warmup period.
+        # Do not allow negative starting time to avoid confusions
+        res = self.__simulation(max(start_time-warmup_period, 0), start_time)
+        # Process result
+        if not isinstance(res, str):
+            # Get result
+            self.__get_results(res, store=True, store_initial=True)
+            # Set internal start time to start_time
+            self.start_time = start_time
+            # Initialize KPI Calculator
+            self.cal.initialize()
+            # Set scenario end flag to false
+            self.scenario_end = False
+            # Get full current state
+            payload = self._get_full_current_state()
+            message = "Test simulation initialized successfully to {0}s with warmup period of {1}s.".format(self.start_time, warmup_period)
+            logging.info(message)
+
+            return status, message, payload
+
+        else:
+            payload = None
+            status = 500
+            message = "Failed to initialize test simulation: {}.".format(res)
+            logging.error(message)
+
+            return status, message, payload
+
+    def get_step(self):
+        '''Returns the current control step in seconds.
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        status: int
+            Indicates whether a request for querying the control step has been completed.
+            If 200, the step was successfully queried.
+            If 500, an internal error occurred.
+        message: str
+            Includes detailed debugging information.
+        payload: int
+            The current control step.
+            None if error during query.
+
+        '''
+
+        status = 200
+        message = "Queried the control step successfully."
+        payload = None
+        try:
+            payload = self.step
+            logging.info(message)
+        except:
+            status = 500
+            message = "Failed to query the simulation step: {}".format(traceback.format_exc())
+            logging.error(message)
+
+        return status, message, payload
+
+    def set_step(self, step):
+        '''Sets the control step in seconds.
+
+        Parameters
+        ----------
+        step: int or float
+            Control step in seconds.
+
+        Returns
+        -------
+        status: int
+            Indicates whether a request for setting the control step has been completed.
+            If 200, the step was successfully set.
+            If 400, an invalid simulation step (non-numeric) was identified.
+            If 500, an internal error occurred.
+        message: str
+            Includes detailed debugging information.
+        payload:
+            None
+
+        '''
+
+        status = 200
+        message = "Control step set successfully."
+        payload = None
+        try:
+            step = float(step)
+        except:
+            payload = None
+            status = 400
+            message = "Invalid value {} for parameter step. Value must be a float, integer, or string able to be converted to a float, but is {}.".format(step, type(step))
+            logging.error(message)
+            return status, message, payload
+        if step < 0:
+            payload = None
+            status = 400
+            message = "Invalid value {} for parameter step. Value must not be negative.".format(step)
+            logging.error(message)
+            return status, message, payload
+        try:
+            self.step = step
+        except:
+            payload = None
+            status = 500
+            message = "Failed to set the control step: {}".format(traceback.format_exc())
+            logging.error(message)
+            return status, message, payload
+        payload={'step':self.step}
+        logging.info(message)
+
+        return status, message, payload
+
+    def get_inputs(self):
+        '''Returns a dictionary of control inputs and their meta-data.
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        status: int
+            Indicates whether a request for querying the inputs has been completed.
+            If 200, the inputs were successfully queried.
+            If 500, an internal error occurred.
+        message: str
+            Includes detailed debugging information.
+        payload: dict
+            Dictionary of control inputs and their meta-data.
+            Returns None if error in getting inputs and meta-data.
+
+        '''
+
+        status = 200
+        message = "Queried the inputs successfully."
+        payload = None
+        try:
+            payload = self.inputs_metadata
+            logging.info(message)
+        except:
+            status = 500
+            message = "Failed to query the input list: {}".format(traceback.format_exc())
+            logging.error(message)
+
+        return status, message, payload
+
+    def get_measurements(self):
+        '''Returns a dictionary of measurements and their meta-data.
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        status: int
+            Indicates whether a request for querying the outputs has been completed.
+            If 200, the outputs were successfully queried.
+            If 500, an internal error occurred.
+        message: str
+            Includes detailed debugging information.
+        payload : dict
+            Dictionary of measurements and their meta-data.
+            Returns None if error in getting measurements and meta-data.
+
+        '''
+
+        status = 200
+        message = "Queried the measurements successfully."
+        payload = None
+        try:
+            payload = self.outputs_metadata
+            logging.info(message)
+        except:
+            status = 500
+            message = "Failed to query the measurement list: {}".format(traceback.format_exc())
+            logging.error(message)
+
+        return status, message, payload
+
+    def get_results(self, point_names, start_time, final_time):
+        '''Returns measurement and control input trajectories.
+
+        Parameters
+        ----------
+        point_names: list
+            Variable names.
+        start_time : int or float
+            Start time of data to return in seconds.
+        final_time : int or float
+            Start time of data to return in seconds.
+
+        Returns
+        -------
+        status: int
+            Indicates whether a request for querying the results has been completed.
+            If 200, the results were successfully queried.
+            If 400, invalid start time and/or invalid final time (non-numeric) were identified.
+            If 500, an internal error occured.
+        message: str
+            Includes detailed debugging information.
+        payload : dict
+            Dictionary of variable trajectories with time as lists.
+            {'time':[<time_data>],
+             'var':[<var_data>]
+            }
+            Returns None if no variable can be found or a simulation error occurs.
+
+        '''
+
+        status = 200
+        try:
+            start_time = float(start_time)
+        except:
+            payload = None
+            status = 400
+            message = "Invalid value {} for parameter start_time. Value must be a float, integer, or string able to be converted to a float, but is {}.".format(start_time, type(start_time))
+            logging.error(message)
+            return status, message, payload
+        try:
+            final_time = float(final_time)
+        except:
+            payload = None
+            status = 400
+            message = "Invalid value {} for parameter final_time. Value must be a float, integer, or string able to be converted to a float, but is {}.".format(final_time, type(final_time))
+            logging.error(message)
+            return status, message, payload
+        payload = {}
+        try:
+            for point_name in point_names:
+                # Get correct points
+                if point_name in self.y_store.keys():
+                    payload[point_name] = self.y_store[point_name]
+                elif point_name in self.u_store.keys():
+                    payload[point_name] = self.u_store[point_name]
+                else:
+                    status = 400
+                    message = "Invalid point name {} in parameter point_names.  Check lists of available inputs and measurements.".format(point_name)
+                    logging.error(message)
+                    return status, message, None
+            if any(item in point_names for item in self.y_store.keys()):
+                payload['time'] = self.y_store['time']
+            elif any(item in point_names for item in self.u_store.keys()):
+                payload['time'] = self.u_store['time']
+            # Get correct time
+            if payload and 'time' in payload:
+                # Find min and max time
+                min_t = min(payload['time'])
+                max_t = max(payload['time'])
+                # If min time is before start time
+                if min_t < start_time:
+                    # Check if start time in time array
+                    if start_time in payload['time']:
+                        t1 = start_time
+                    # Otherwise, find first time in time array after start time
+                    else:
+                        np_t = np.array(payload['time'])
+                        t1 = np_t[np_t>=start_time][0]
+                # Otherwise, first time is min time
+                else:
+                    t1 = min_t
+                # If max time is after final time
+                if max_t > final_time:
+                    # Check if final time in time array
+                    if final_time in payload['time']:
+                        t2 = final_time
+                    # Otherwise, find last time in time array before final time
+                    else:
+                        np_t = np.array(payload['time'])
+                        t2 = np_t[np_t<=final_time][-1]
+                # Otherwise, last time is max time
+                else:
+                    t2 = max_t
+                # Use found first and last time to find corresponding indecies
+                i1 = payload['time'].index(t1)
+                i2 = payload['time'].index(t2)+1
+                for key in (point_names +['time']):
+                    payload[key] = payload[key][i1:i2]
+        except:
+            status = 500
+            message = "Failed to query simulation results: {}".format(traceback.format_exc())
+            logging.error(message)
+            return status, message, None
+
+        if not isinstance(payload, (list, type(None))):
+            for key in payload:
+                payload[key] = payload[key].tolist()
+
+        message = "Queried results data successfully for point names {}.".format(point_names)
+        logging.info(message)
+        return status, message, payload
+
+    def get_kpis(self):
+        '''Returns KPI data.
+
+        Requires standard sensor signals.
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        status: int
+            Indicates whether a request for querying the KPIs has been completed.
+            If 200, the KPIs were successfully queried.
+            If 500, an internal error occured.
+        message: str
+            Includes detailed debugging information
+        payload : dict
+            Dictionary containing KPI names and values.
+            {<kpi_name>:<kpi_value>}.
+            Returns None if error during calculation.
+
+        '''
+
+        status = 200
+        message = "Queried KPIs successfully."
+        try:
+            # Set correct price scenario for cost
+            if self.scenario['electricity_price'] == 'constant':
+                price_scenario = 'Constant'
+            elif self.scenario['electricity_price'] == 'dynamic':
+                price_scenario = 'Dynamic'
+            elif self.scenario['electricity_price'] == 'highly_dynamic':
+                price_scenario = 'HighlyDynamic'
+            # Calculate the core kpis
+            payload = self.cal.get_core_kpis(price_scenario=price_scenario)
+        except:
+            payload = None
+            status = 500
+            message = "Failed to query KPIs: {}".format(traceback.format_exc())
+            logging.error(message)
+        logging.info(message)
+
+        return status, message, payload
+
+    def get_forecast_points(self):
+        '''Returns a dictionary of available forecast points and their meta-data.
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        status: int
+            Indicates whether a request for querying the forecast points has been completed.
+            If 200, the outputs were successfully queried.
+            If 500, an internal error occurred.
+        message: str
+            Includes detailed debugging information.
+        payload : dict
+            Dictionary of forecast points and their meta-data.
+            Returns None if error in getting forecast points and meta-data.
+
+        '''
+
+        # Get the forecast
+        status = 200
+        message = "Queried the forecast points and their meta-data successfully."
+        try:
+            payload = self.forecasts_metadata
+        except:
+            status = 500
+            message = "Failed to query the test case forecast points and their meta-data: {}".format(traceback.format_exc())
+            payload = None
+            logging.error(message)
+        logging.info(message)
+
+        return status, message, payload
+
+    def get_forecast(self, point_names, horizon, interval, temperature_uncertainty=None, solar_uncertainty=None):
+        '''Returns the test case data forecast
+
+        Parameters
+        ----------
+        point_names : list of str
+            List of forecast point names for which to get data.
+        horizon: int or float
+            Forecast horizon in seconds.
+        interval: int or float
+            Forecast interval in seconds.
+
+        Returns
+        -------
+        status: int
+            Indicates whether a request for querying the forecast has been successfully completed.
+            If 200, the forecast was successfully queried.
+            If 500, an internal error occurred.
+        message: str
+            Includes detailed debugging information
+        payload: dict
+            Dictionary with the requested forecast data
+            {<variable_name>:<variable_forecast_trajectory>}
+            where <variable_name> is a string with the variable
+            key and <variable_forecast_trajectory> is a list with
+            the forecasted values. 'time' is included as a variable.
+
+        '''
+
+
+        # Get the forecast
+        status = 200
+        message = "Queried the forecast data successfully."
+        if 'temperature_uncertainty' in self.scenario or 'solar_uncertainty' in self.scenario:
+            if (self.scenario.get('temperature_uncertainty') and temperature_uncertainty) or (self.scenario.get('solar_uncertainty') and solar_uncertainty):
+                payload = None
+                status = 400
+
+                message = "The scenario configuration already includes specific uncertainty settings. "
+                if (self.scenario.get('temperature_uncertainty') and temperature_uncertainty):
+                    message += "'temperature_uncertainty' is already set to '{}', further overriding of 'temperature_uncertainty' is not allowed. ".format(
+                        self.scenario['temperature_uncertainty'])
+                if (self.scenario.get('solar_uncertainty') and solar_uncertainty):
+                    message += "'solar_uncertainty' is already set to '{}', further overriding of 'solar_uncertainty' is not allowed. ".format(
+                        self.scenario['solar_uncertainty'])
+        # if 'solar_uncertainty' in self.scenario:
+        #     if (self.scenario['solar_uncertainty'] and solar_uncertainty):
+
+
+
+                logging.error(message)
+                return status, message, payload
+
+            if self.scenario.get('temperature_uncertainty'):
+                temperature_uncertainty=self.scenario['temperature_uncertainty']
+            if self.scenario.get('solar_uncertainty'):
+                solar_uncertainty=self.scenario['solar_uncertainty']
+
+        # Check inputs
+        try:
+            horizon = float(horizon)
+        except:
+            payload = None
+            status = 400
+            message = "Invalid value {} for parameter horizon. Value must be a float, integer, or string able to be converted to a float, but is {}.".format(horizon, type(horizon))
+            logging.error(message)
+            return status, message, payload
+        try:
+            interval = float(interval)
+        except:
+            payload = None
+            status = 400
+            message = "Invalid value {} for parameter interval. Value must be a float, integer, or string able to be converted to a float, but is {}.".format(interval, type(interval))
+            logging.error(message)
+            return status, message, payload
+
+        if interval <= 0:
+            payload = None
+            status = 400
+            message = "Invalid value {} for parameter interval. Value must be positive.".format(interval)
+            logging.error(message)
+            return status, message, payload
+            # Check the temperature_uncertainty value
+        # if temperature_uncertainty and 'TDryBul' not in point_names:
+        #     payload = None
+        #     status = 400
+        #     message = "Temperature uncertainty provided but 'TDryBul' is missing in point_names."
+        #     logging.error(message)
+        #     return status, message, payload
+
+        # if solar_uncertainty and 'HGloHor' not in point_names:
+        #     payload = None
+        #     status = 400
+        #     message = "Solar uncertainty provided but 'HGloHor' is missing in point_names."
+        #     logging.error(message)
+        #     return status, message, payload
+        allowed_uncertainties = [None, 'low', 'medium', 'high']
+        if temperature_uncertainty not in allowed_uncertainties:
+            payload = None
+            status = 400
+            message = "Invalid value for temperature_uncertainty. Allowed values are: {}".format(
+                allowed_uncertainties[1:])
+            logging.error(message)
+            return status, message, payload
+
+        # Check the solar_uncertainty value
+        if solar_uncertainty not in allowed_uncertainties:
+            payload = None
+            status = 400
+            message = "Invalid value for solar_uncertainty. Allowed values are: {}".format(allowed_uncertainties[1:])
+            logging.error(message)
+            return status, message, payload
+
+        wrong_points = []
+        for point in point_names:
+            if point not in self.forecast_names:
+                wrong_points.append(str(point))
+        if wrong_points:
+            payload = None
+            status = 400
+            message = "Invalid point name(s) {} in parameter point_names.  Check list of available forecast points.".format(wrong_points)
+            logging.error(message)
+            return status, message, payload
+        # Check for missing point_names related to uncertainty parameters
+
+        temperature_params = {
+            "F0": 0, "K0": 0, "F": 0, "K": 0, "mu": 0
+        }
+
+        solar_params = {
+            "ag0": 0, "bg0": 0, "phi": 0, "ag": 0, "bg": 0
+        }
+
+        if temperature_uncertainty is not None:
+            temperature_params.update(self.uncertainty_params['temperature'][temperature_uncertainty])
+
+        if solar_uncertainty is not None:
+            solar_params.update(self.uncertainty_params['solar'][solar_uncertainty])
+
+        try:
+
+            payload = self.forecaster.get_forecast(
+                point_names,
+                horizon=horizon,
+                interval=interval,
+                weather_temperature_dry_bulb=temperature_params,
+                weather_solar_global_horizontal=solar_params
+            )
+
+        except:
+            status = 500
+            message = "Failed to query the test case forecast data: {}".format(traceback.format_exc())
+            payload = None
+            logging.error(message)
+        logging.info(message)
+
+        return status, message, payload
+
+    def set_scenario(self, scenario):
+        '''Sets the test case scenario.
+
+        Parameters
+        ----------
+        scenario : dict
+            {'electricity_price': <'constant' or 'dynamic' or 'highly_dynamic'>,
+             'time_period': see available <str> keys for test case,
+             'temperature_uncertainty':<'low' or 'medium' or 'high'>,
+             'solar_uncertainty':<'low' or 'medium' or 'high'>
+            }
+            If any value is None, it will not change existing.
+
+        Returns
+        -------
+        status: int
+            Indicates whether a request for setting the scenario has been completed
+            If 200, the scenario was successfully set.
+            If 400, invalid electricity_price and/or time_period (non-numeric) were identified.
+            If 500, an internal error occurred.
+        message: str
+            Includes the detailed debug information
+        payload: dict
+            {'electricity_price': if succeeded in changing then True, else None,
+             'time_period': if succeeded then initial measurements, else None,
+             'temperature_uncertainty': if succeeded in changing then True, else None,
+             'solar_uncertainty': if succeeded in changing then True, else None
+            }
+        '''
+
+        status = 200
+        message = "Test case scenario was set successfully."
+        payload = {
+            'electricity_price': None,
+            'time_period': None,
+            'temperature_uncertainty': None,
+            'solar_uncertainty': None
+        }
+        if not hasattr(self, 'scenario'):
+            self.scenario = {}
+        try:
+            # Handle electricity price
+            if scenario['electricity_price']:
+                if scenario['electricity_price'] not in ['constant', 'dynamic', 'highly_dynamic']:
+                    status = 400
+                    message = "Scenario parameter electricy_price is {}, " \
+                              "but should be 'constant', 'dynamic', or 'highly_dynamic'.". \
+                              format(scenario['electricity_price'])
+                    logging.error(message)
+                    return status, message, payload
+                self.scenario['electricity_price'] = scenario['electricity_price']
+                payload['electricity_price'] = self.scenario['electricity_price']
+            # Handle timeperiod
+            if scenario['time_period']:
+                if scenario['time_period'] not in self.days_json:
+                    status = 400
+                    message = "Scenario parameter time_period is {}, but " \
+                              "should be one of the following: {}.". \
+                              format(scenario['time_period'], list(self.days_json.keys()))
+                    logging.error(message)
+                    return status, message, payload
+                self.scenario['time_period'] = scenario['time_period']
+                warmup_period = 7*24*3600.
+                key = self.scenario['time_period']
+                start_time = self.days_json[key]*24*3600.-7*24*3600.
+                end_time = start_time + 14*24*3600.
+
+            # Handle temperature uncertainty
+
+
+
+            if scenario['temperature_uncertainty']:
+                if scenario['temperature_uncertainty'] not in ['low', 'medium', 'high']:
+                    status = 400
+                    message = "Scenario parameter temperature_uncertainty is {}, " \
+                              "but should be 'low', 'medium' or 'high'.". \
+                              format(scenario['temperature_uncertainty'])
+                    logging.error(message)
+                    return status, message, payload
+                self.scenario['temperature_uncertainty'] = scenario['temperature_uncertainty']
+                payload['temperature_uncertainty'] = self.scenario['temperature_uncertainty']
+            else:
+                self.scenario['temperature_uncertainty'] = None
+
+            # Handle solar uncertainty
+            if scenario['solar_uncertainty']:
+                if scenario['solar_uncertainty'] not in ['low', 'medium', 'high']:
+                    status = 400
+                    message = "Scenario parameter solar_uncertainty is {}, " \
+                              "but should be 'low', 'medium' or 'high'.". \
+                        format(scenario['solar_uncertainty'])
+                    logging.error(message)
+                    return status, message, payload
+                self.scenario['solar_uncertainty'] = scenario['solar_uncertainty']
+                payload['solar_uncertainty'] = self.scenario['solar_uncertainty']
+            else:
+                self.scenario['solar_uncertainty'] = None
+
+        except:
+            status = 400
+            message = "Invalid values for the scenario parameters: {}".format(traceback.format_exc())
+            logging.error(message)
+            return status, message, payload
+        try:
+            if scenario['time_period']:
+                initialize_payload = self.initialize(start_time, warmup_period, end_time=end_time)
+                if initialize_payload[0] != 200:
+                    status = 500
+                    message = initialize_payload[1]
+                    logging.error(message)
+                    return status, message, payload
+                payload['time_period'] = initialize_payload[2]
+            # It's needed to reset KPI Calculator when scenario is changed
+            self.cal.initialize()
+        except:
+            status = 500
+            message = "Failed to set the test case scenario: {}".format(traceback.format_exc())
+            payload = None
+            logging.error(message)
+        logging.info(message)
+
+        return status, message, payload
+
+    def get_scenario(self):
+        '''Returns the current case scenario.
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        status: int
+            Indicates whether a request for querying the scenario has been successfully completed
+            If 200, the scenario was successfully queried.
+            If 500, an internal error occurred.
+        message: str
+            Includes detailed debugging information
+        payload: dict
+            {'electricity_price': <str>,
+             'time_period': <str>
+             }
+
+        '''
+
+        payload = None
+        status = 200
+        message = "Queried current test case for scenario successfully."
+        try:
+            payload = self.scenario
+        except:
+            status = 500
+            message = "Failed to find a current test case scenario setting.  Check it was set properly: {}".format(traceback.format_exc())
+            logging.error(message)
+        logging.info(message)
+
+        return status, message, payload
+
+    def get_name(self):
+        '''Returns the name of the test case fmu.
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        status: int
+            Indicate whether a request for querying the name of the test case has been successfully completed.
+            If 200, the name was successfully queried.
+            If 500, an internal error occurred.
+        message: str
+            Includes detailed debugging information
+        payload :  dict
+            Name of test case as {'name': <str>}
+
+        '''
+
+        status = 200
+        message = "Queried the name of the test case successfully."
+        payload = {'name': self.name}
+        logging.info(message)
+
+        return status, message, payload
+
+    def get_version(self):
+        '''Returns the version number of BOPTEST.
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        status: int
+            Indicate whether a request for querying the version number of BOPTEST has been completed.
+            If 200, the version was successfully queried.
+            If 500, an internal error occurred.
+        message: str
+            Includes detailed debugging information
+        payload:  dict
+            Version of BOPTEST as {'version': <str>}
+
+        '''
+
+        status = 200
+        message = "Queried the version number successfully."
+        logging.info(message)
+        payload = {'version': self.version}
+
+        return status, message, payload
+
+    def post_results_to_dashboard(self, api_key, tags, unit_test=False):
+        '''Posts test results to online dashboard at given server address.
+
+        Parameters
+        ----------
+        api_key : str
+            API key corresponding to user account for dashboard.
+        tags : list of str
+            List of tags to be included with result posting.
+        unit_test : bool
+            True if API being called for unit testing so as not to post to online dashboard.
+
+        Returns
+        -------
+        status: int
+            Indicates whether a posting to the dashboard has been successfully completed.
+            If 200, the posting was successful.
+            If 400, there was an input error.
+            If 500, an internal error occurred.
+        message: str
+            Includes detailed debugging information
+        payload: None
+
+        '''
+
+        # Check formal scnenario test completed
+        if not self.scenario_end:
+            status = 500
+            message = 'Formal test scenario, including time period, has not been completed.  Initialize a test scenario using the PUT /scenario API and run it to completion before submitting results to dashboard.'
+            logging.error(message)
+            return status, message, None
+        # Check parameters
+        if not isinstance(api_key, str):
+            status = 400
+            message = 'Invalid type for input api_key. It must be a string, but is a {0}.'.format(type(api_key))
+            logging.error(message)
+            return status, message, None
+
+        if not isinstance(tags, list):
+            status = 400
+            message = 'Invalid type for input tags. It must be a list of strings, but is a {0}.'.format(type(tags))
+            logging.error(message)
+            return status, message, None
+
+        if len(tags)>10:
+            status = 400
+            message = 'Invalid number of tags. The limit is 10, but there are {0}.'.format(len(tags))
+            logging.error(message)
+            return status, message, None
+
+        for tag in tags:
+            if not isinstance(tag, str):
+                status = 400
+                message = 'Invalid type for one of the tag inputs. They must be strings, but one is a {0}.'.format(type(tag))
+                logging.error(message)
+                return status, message, None
+        # Specify server address and payload
+        dash_server = os.environ['BOPTEST_DASHBOARD_SERVER']
+        # Create payload
+        uid = str(uuid.uuid4())
+        payload = {
+          "results": [
+            {
+              "uid": uid,
+              "dateRun": str(datetime.now(tz=pytz.UTC)),
+              "boptestVersion": self.version,
+              "isShared": True,
+              "controlStep": str(self.get_step()[2]),
+              "account": {
+                "apiKey": api_key
+              },
+              "forecastParameters":{},
+              "tags": tags,
+              "kpis": self.get_kpis()[2],
+              "scenario": self.add_forecast_uncertainty(self.keys_to_camel_case(self.get_scenario()[2])),
+              "buildingType": {
+                "uid": self.get_name()[2]['name']
+              }
+            }
+          ]
+        }
+        dash_url = "%s/api/results" % dash_server
+        # Post to dashboard
+        if not unit_test:
+            result = requests.post(dash_url, json=payload)
+        else:
+            message = '/submit API run in unit test mode'
+            logging.info(message)
+            status = 200
+            payload_ret = {'dash_url':dash_url, 'payload':payload}
+            return status, message, payload_ret
+
+        # Interpret response
+        status = result.status_code
+        if status == 200:
+            message = 'Results submitted successfully to dashboard at {0}.'.format(dash_server)
+            logging.info(message)
+            payload = {'identifier':uid}
+        else:
+            if 'Could not find any entity of type "accounts" matching' in result.json()['rejected'][0]['message']:
+                message = 'Error submitting results to dashboard at {0}. Check the dashboard user account API key is correct. Full dashboard response is: {1}.'.format(dash_server, result.json())
+            else:
+                message = 'Error submitting results to dashboard at {0}. Full dashboard response is: {1}.'.format(dash_server, result.json())
+            logging.error(message)
+            payload = None
+
+        return status, message, payload
+
+    def _get_elapsed_control_time_ratio(self):
+        '''Returns the elapsed control time ratio vector for the case.
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        elapsed_control_time_ratio : np array of floats
+            elapsed_control_time_ratio for each control step.
+
+        '''
+
+        elapsed_control_time_ratio = self.elapsed_control_time_ratio
+
+        return elapsed_control_time_ratio
+
+    def _get_var_metadata(self, fmu, var_list, inputs=False):
+        '''Build a dictionary of variables and their metadata.
+
+        Parameters
+        ----------
+        fmu : pyfmi fmu object
+            FMU from which to get variable metadata
+        var_list : list of str
+            List of variable names
+
+        Returns
+        -------
+        var_metadata : dict
+            Dictionary of variable names as keys and metadata as fields.
+            {<var_name_str> :
+                "Unit" : str,
+                "Description" : str,
+                "Minimum" : float,
+                "Maximum" : float
+            }
+
+        '''
+
+        # Inititalize
+        var_metadata = dict()
+        # Get metadata
+        for var in var_list:
+            # Units
+            if var == 'time':
+                unit = 's'
+                description = 'Time of simulation'
+                mini = None
+                maxi = None
+            elif '_activate' in var:
+                unit = None
+                description = fmu.get_variable_description(var)
+                mini = None
+                maxi = None
+            else:
+                unit = fmu.get_variable_unit(var)
+                description = fmu.get_variable_description(var)
+                if inputs:
+                    mini = fmu.get_variable_min(var)
+                    maxi = fmu.get_variable_max(var)
+                else:
+                    mini = None
+                    maxi = None
+            var_metadata[var] = {'Unit':unit,
+                                 'Description':description,
+                                 'Minimum':mini,
+                                 'Maximum':maxi}
+
+        return var_metadata
+
+    def _check_value_min_max(self, var, value):
+        '''Check that the input value does not violate the min or max.
+
+        Note that if it does, the value is truncated to the minimum or maximum.
+
+        Parameters
+        ----------
+        var : str
+            Name of variable
+        value : numeric
+            Specified value of variable
+
+        Return
+        ------
+        checked_value : float
+            Value of variable truncated by min and max.
+        message: str
+            Alert messages that input value is truncated to min/max
+        '''
+
+        # Get minimum and maximum for variable
+        mini = self.inputs_metadata[var]['Minimum']
+        maxi = self.inputs_metadata[var]['Maximum']
+        message = None
+        # Check the value and truncate if necessary
+        if value > maxi:
+            checked_value = maxi
+            message = 'WARNING: value of {0} for {1} is above maximum of {2}.  Using {2}. '.format(value, var, maxi)
+        elif value < mini:
+            checked_value = mini
+            message = 'WARNING: value of {0} for {1} is below minimum of {2}.  Using {2}. '.format(value, var, mini)
+        else:
+            checked_value = value
+
+        return checked_value, message
+
+    def _get_area(self):
+        '''Get the building floor area in m^2.
+
+        Returns
+        -------
+        area: float
+            Building floor area in m^2
+
+        '''
+
+        area = self.area
+
+        return area
+
+    def _get_full_current_state(self):
+        '''Combines the self.y and self.u dictionaries into one.
+
+        Returns
+        -------
+        z: dict
+            Combination of self.y and self.u dictionaries.
+
+        '''
+
+        z = self.y.copy()
+        z.update(self.u)
+
+        return z
+
+    def to_camel_case(self, snake_str):
+        components = snake_str.split('_')
+        # We capitalize the first letter of each component except the first one
+        # with the 'title' method and join them together.
+        return components[0] + ''.join(x.title() for x in components[1:])
+
+    def keys_to_camel_case(self, a_dict):
+        result = {}
+        for key, value in a_dict.items():
+            result[self.to_camel_case(key)] = value
+        return result
+
+    # weatherForecastUncertainty is required by the dashboard,
+    # however some testcases don't report it.
+    # This is a workaround
+    def add_forecast_uncertainty(self, scenario):
+        if not 'weatherForecastUncertainty' in scenario:
+            scenario['weatherForecastUncertainty'] = 'deterministic'
+
+        return scenario
+
+    def load_uncertainty_params(self, filepath='forecast/forecast_uncertainty_params.json'):
+        '''Load the uncertainty parameters from a JSON file.
+
+        Parameters
+        ----------
+        filepath : str, optional
+            Path to the JSON file containing the uncertainty parameters.
+            Default is 'forecast_uncertainty_params.json'.
+
+        Returns
+        -------
+        dict
+            Uncertainty parameters loaded from the JSON file.
+        '''
+        with open(filepath, 'r') as f:
+            uncertainty_params = json.load(f)
+        return uncertainty_params
\ No newline at end of file
diff --git a/testing/testcase2/version.txt b/testing/testcase2/version.txt
new file mode 100644
index 000000000..4259c642a
--- /dev/null
+++ b/testing/testcase2/version.txt
@@ -0,0 +1 @@
+0.5.0-dev
diff --git a/testing/testcase3/README.md b/testing/testcase3/README.md
new file mode 100644
index 000000000..76af5cceb
--- /dev/null
+++ b/testing/testcase3/README.md
@@ -0,0 +1,24 @@
+BOPTEST Test Case 3
+===================
+This test is a simple RC network with two zones for multi-zone testing.
+
+Structure
+---------
+
+- ``doc/`` contains documentation of the test case.
+- ``models/`` contains the emulation model files for the test case.
+- ``config.py`` defines the configuration of the test case for building the Docker image.
+
+Compile the Model
+-----------------
+
+The Modelica model makes use of the signal overwrite blocks and uses the
+overwrite block parser (``<repo_root>/parser/parser.py``) to compile the FMU.
+
+1. Add the root of the BOPTEST repository to the PYTHONPATH environment variable.
+
+2. ``$ cd models``
+
+3. ``$ python compile_fmu.py`` to compile the model FMU using the overwrite block parser.
+
+4. The resulting FMU will be named ``wrapped.fmu``.
diff --git a/testing/testcase3/__init__.py b/testing/testcase3/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/testing/testcase3/forecast/README.md b/testing/testcase3/forecast/README.md
new file mode 100644
index 000000000..d6fefe2dd
--- /dev/null
+++ b/testing/testcase3/forecast/README.md
@@ -0,0 +1,13 @@
+This directory contains the Forecaster module to retrieve forecast
+from a BOPTEST test case, which is needed for predictive controllers.
+This module uses the data_manager object of a test case to read the
+deterministic forecast from the test case FMU. In future
+developments it will be possible to request stochastic forecast with
+a predefined distribution over the deterministic forecast for research
+purposes. This distribution will be added on the top of the
+deterministic forecast mentioned before.
+
+The controller developer can choose the prediction horizon and
+interval of the forecast from the actual simulation time. The
+controller developer may also filter the forecast for a specific data
+category or request all data variables and filter it afterwards.
diff --git a/testing/testcase3/forecast/__init__.py b/testing/testcase3/forecast/__init__.py
new file mode 100644
index 000000000..29e4d987e
--- /dev/null
+++ b/testing/testcase3/forecast/__init__.py
@@ -0,0 +1,6 @@
+'''
+Created on Apr 25, 2019
+
+@author: Javier Arroyo
+
+'''
diff --git a/testing/testcase3/forecast/error_emulator.py b/testing/testcase3/forecast/error_emulator.py
new file mode 100644
index 000000000..ec9f100f6
--- /dev/null
+++ b/testing/testcase3/forecast/error_emulator.py
@@ -0,0 +1,86 @@
+'''
+Created on Sept 5, 2022
+
+@author: Laura Zabala
+
+'''
+
+import numpy as np
+
+
+
+def mean_filter(data, window_size=3):
+    """
+    Apply mean filter to a 1D list of data.
+
+    Parameters:
+    - data: List of numbers to be filtered.
+    - window_size: Size of the filtering window. Must be an odd number.
+
+    Returns:
+    - Filtered data.
+    """
+    if window_size % 2 == 0:
+        raise ValueError("Window size must be an odd number.")
+
+    half_window = window_size // 2
+    filtered_data = data.copy()
+
+    for i in range(half_window, len(data) - half_window):
+        if data[i] == 0:
+            continue
+
+        window_data = data[i - half_window : i + half_window + 1]
+        filtered_data[i] = sum(window_data) / len(window_data)
+
+    return filtered_data
+def predict_temperature_error_AR1(hp, F0, K0, F, K, mu):
+    '''Generates an error with an AR model in the hp points of the predictions horizon.
+
+    Parameters
+    ----------
+    hp : int, number of points in the prediction horizon
+    F0 : float, mean of the initial error model
+    K0 : float, standard deviation of the initial error model
+    F : float from 0 to 1, autocorrelation factor of the AR error model
+    K : float, standard deviation of the AR error model
+    mu : float, mean value of the distribution function integrated in the AR error model
+
+    Returns
+    -------
+    error : 1D array with the error values in the hp points
+
+    '''
+    hp=int(hp)
+    error = np.zeros(hp)
+    error[0,] = np.random.normal(F0, K0)
+    for i_c in range(hp - 1):
+        error[i_c + 1] = np.random.normal(
+            error[i_c] * F + mu, K
+        )
+    return error
+
+
+
+def predict_solar_error_AR1(hp, ag0, bg0, phi, ag, bg):
+    '''Generates an error based on the specified parameters using an AR model in the hp points of the predictions horizon.
+
+        Parameters
+        ----------
+        hp : int
+            Number of points in the prediction horizon.
+        ag0, bg0, phi, ag, bg : float
+            Parameters for the AR1 model.
+
+        Returns
+        -------
+        error : 1D numpy array
+            Contains the error values in the hp points.
+    '''
+    hp = int(hp)
+    error = np.zeros(hp)
+    error[0] = np.random.laplace(ag0, bg0)
+    for i_c in range(1, hp):
+        error[i_c] = np.random.laplace(error[i_c - 1] * phi + ag, bg)
+
+    return error
diff --git a/testing/testcase3/forecast/forecast_uncertainty_params.json b/testing/testcase3/forecast/forecast_uncertainty_params.json
new file mode 100644
index 000000000..f2b99db71
--- /dev/null
+++ b/testing/testcase3/forecast/forecast_uncertainty_params.json
@@ -0,0 +1,49 @@
+{
+    "temperature": {
+        "low": {
+            "F0": 0,
+            "K0": 0.6,
+            "F": 0.92,
+            "K": 0.4,
+            "mu": 0
+        },
+        "medium": {
+            "F0": 0.15,
+            "K0": 1.2,
+            "F": 0.93,
+            "K": 0.6,
+            "mu": 0
+        },
+        "high": {
+            "F0": -0.58,
+            "K0": 1.5,
+            "F": 0.95,
+            "K": 0.7,
+            "mu": -0.015
+        }
+    },
+    "solar": {
+        "low": {
+            "ag0": 4.44,
+            "bg0": 57.42,
+            "phi": 0.62,
+            "ag": 1.86,
+            "bg": 45.64
+        },
+        "medium": {
+            "ag0": 15.02,
+            "bg0": 122.6,
+            "phi": 0.63,
+            "ag": 4.44,
+            "bg": 91.97
+        },
+        "high": {
+            "ag0": 32.09,
+            "bg0": 119.94,
+            "phi": 0.67,
+            "ag": 10.63,
+            "bg": 87.44
+        }
+    }
+
+}
diff --git a/testing/testcase3/forecast/forecaster.py b/testing/testcase3/forecast/forecaster.py
new file mode 100644
index 000000000..e0ba1640c
--- /dev/null
+++ b/testing/testcase3/forecast/forecaster.py
@@ -0,0 +1,99 @@
+'''
+Created on Apr 25, 2019
+
+@author: Javier Arroyo
+
+This module contains the Forecaster class with methods to obtain
+forecast data for the test case. It relies on the data_manager object
+of the test case to provide deterministic forecast.
+
+'''
+from .error_emulator import predict_temperature_error_AR1, predict_solar_error_AR1,mean_filter
+import numpy as np
+
+
+class Forecaster(object):
+    '''This class retrieves test case data forecast for its use in
+    optimal control strategies.
+
+    '''
+
+    def __init__(self, testcase):
+        '''
+        Constructor
+
+        Parameters
+        ----------
+        testcase: BOPTEST TestCase object
+            object of an already deployed test case that
+            contains the data stored from the test case run
+
+        '''
+
+        # Point to the test case object
+        self.case = testcase
+
+    def get_forecast(self, point_names, horizon=24 * 3600, interval=3600,
+                     weather_temperature_dry_bulb=None, weather_solar_global_horizontal=None,
+                     category=None, plot=False):
+
+        if weather_temperature_dry_bulb is None:
+            weather_temperature_dry_bulb = {
+                "F0": 0, "K0": 0, "F": 0, "K": 0, "mu": 0
+            }
+
+        if weather_solar_global_horizontal is None:
+            weather_solar_global_horizontal = {
+                "ag0": 0, "bg0": 0, "phi": 0, "ag": 0, "bg": 0
+            }
+        # Get the forecast
+        forecast = self.case.data_manager.get_data(variables=point_names,
+                                                   horizon=horizon,
+                                                   interval=interval,
+                                                   category=category,
+                                                   plot=plot)
+        if 'TDryBul' in point_names and any(weather_temperature_dry_bulb.values()):
+            # error in the forecast
+            error_forecast_temp = predict_temperature_error_AR1(
+                hp=horizon / interval + 1,
+                F0=weather_temperature_dry_bulb["F0"],
+                K0=weather_temperature_dry_bulb["K0"],
+                F=weather_temperature_dry_bulb["F"],
+                K=weather_temperature_dry_bulb["K"],
+                mu=weather_temperature_dry_bulb["mu"]
+            )
+
+            # forecast error just added to dry bulb temperature
+            forecast['TDryBul'] = forecast['TDryBul'] + error_forecast_temp
+            forecast['TDryBul']=forecast['TDryBul'].tolist()
+        if 'HGloHor' in point_names and any(weather_solar_global_horizontal.values()):
+
+            original_HGloHor = np.array(forecast['HGloHor']).copy()
+            lower_bound = 0.2 * original_HGloHor
+            upper_bound = 2 * original_HGloHor
+            indices = np.where(original_HGloHor > 50)[0]
+            for _ in range(200):
+            # while True:
+                error_forecast_solar = predict_solar_error_AR1(
+                    horizon / interval + 1,
+                    weather_solar_global_horizontal["ag0"],
+                    weather_solar_global_horizontal["bg0"],
+                    weather_solar_global_horizontal["phi"],
+                    weather_solar_global_horizontal["ag"],
+                    weather_solar_global_horizontal["bg"]
+                )
+
+                forecast['HGloHor'] = original_HGloHor + error_forecast_solar
+
+                # Check if any point in forecast['HGloHor'] is out of the specified range
+
+                condition = np.any((forecast['HGloHor'][indices] > 2 * original_HGloHor[indices]) |
+                               (forecast['HGloHor'][indices] < 0.2 * original_HGloHor[indices]))
+                # forecast['HGloHor']=gaussian_filter_ignoring_nans(forecast['HGloHor'])
+                forecast['HGloHor'] = mean_filter(forecast['HGloHor'])
+                forecast['HGloHor'] = np.clip(forecast['HGloHor'], lower_bound, upper_bound)
+                forecast['HGloHor']=forecast['HGloHor'].tolist()
+                if not condition:
+                    break
+
+        return forecast
diff --git a/testing/testcase3/models/wrapped.fmu b/testing/testcase3/models/wrapped.fmu
new file mode 100644
index 000000000..fe4f08b3a
Binary files /dev/null and b/testing/testcase3/models/wrapped.fmu differ
diff --git a/testing/testcase3/testcase.py b/testing/testcase3/testcase.py
new file mode 100644
index 000000000..8e97f2e57
--- /dev/null
+++ b/testing/testcase3/testcase.py
@@ -0,0 +1,1482 @@
+# -*- coding: utf-8 -*-
+"""
+This module defines the API to the test case used by the REST requests to
+perform functions such as advancing the simulation, retrieving test case
+information, and calculating and reporting results.
+
+"""
+
+from pyfmi import load_fmu
+import numpy as np
+import copy
+import time
+from data.data_manager import Data_Manager
+from forecast.forecaster import Forecaster
+from kpis.kpi_calculator import KPI_Calculator
+import requests
+import traceback
+import logging
+import pytz
+from datetime import datetime
+import uuid
+import os
+import json
+import array as a
+
+class TestCase(object):
+    '''Class that implements the test case.
+
+    '''
+
+    def __init__(self, fmupath='models/wrapped.fmu'):
+        '''Constructor.
+
+        Parameters
+        ----------
+        fmupath : str, optional
+            Path to the test case fmu.
+            Default is assuming a particular directory structure.
+
+        '''
+
+        # Set BOPTEST version number
+        with open('version.txt', 'r') as f:
+            self.version = f.read()
+        # Set test case fmu path and check if path exists and throw execption
+        self.fmupath = fmupath
+        if not os.path.exists(fmupath) or not os.path.isfile(fmupath):
+            raise Exception("The test case FMU cannot be found. Check TESTCASE name entered correctly.")
+        # Instantiate a data manager for this test case
+        self.data_manager = Data_Manager(testcase=self)
+        # Load data and the kpis_json for the test case
+        self.data_manager.load_data_and_jsons()
+        # Instantiate a forecaster for this test case
+        self.forecaster = Forecaster(testcase=self)
+        # Define name
+        self.name = self.config_json['name']
+        # Load fmu
+        self.fmu = load_fmu(self.fmupath)
+        self.fmu.set_log_level(7)
+        # Configure the log, log file, and console output
+        name = 'boptest_{0}'.format(self.name)
+        fmt = '%(asctime)s UTC\t%(name)-20s%(levelname)s\t%(message)s'
+        datefmt = '%m/%d/%Y %I:%M:%S %p'
+        formatter = logging.Formatter(fmt,datefmt)
+        logging.basicConfig(filename='{0}.log'.format(name), filemode='w', level=10, format=fmt, datefmt=datefmt)
+        logger = logging.getLogger()
+        stream_handler = logging.StreamHandler()
+        stream_handler.setFormatter(formatter)
+        logger.addHandler(stream_handler)
+        # Get version and check is 2.0
+        self.fmu_version = self.fmu.get_version()
+        if self.fmu_version != '2.0':
+            raise ValueError('FMU must be version 2.0.')
+        # Get building area
+        self.area = self.config_json['area']
+        # Get available control inputs and outputs
+        self.input_names = list(self.fmu.get_model_variables(causality = 2).keys())
+        self.output_names = list(self.fmu.get_model_variables(causality = 3).keys())
+        self.forecast_names = list(self.data.keys())
+        # Set default communication step
+        self.set_step(self.config_json['step'])
+        # Initialize simulation data arrays
+        self.__initilize_data()
+        # Set default fmu simulation options
+        self.options = self.fmu.simulate_options()
+        self.options['filter'] = self.output_names + self.input_names
+        # Instantiate a KPI calculator for the test case
+        self.cal = KPI_Calculator(testcase=self)
+        # Initialize test case
+        self.initialize(self.config_json['start_time'], self.config_json['warmup_period'])
+        # Set default scenario
+        self.config_json['scenario']['solar_uncertainty']=None #todo
+        self.config_json['scenario']['temperature_uncertainty']=None #todo
+        self.set_scenario(self.config_json['scenario'])
+        self.uncertainty_params = self.load_uncertainty_params()
+
+    def __initilize_data(self):
+        '''Initializes objects for simulation data storage.
+
+        Uses self.output_names and self.input_names to create
+        self.y, self.y_store, self.u, self.u_store,
+        self.inputs_metadata, self.outputs_metadata.
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        None
+
+        '''
+
+        # Get input and output and forecast meta-data
+        self.inputs_metadata = self._get_var_metadata(self.fmu, self.input_names, inputs=True)
+        self.outputs_metadata = self._get_var_metadata(self.fmu, self.output_names)
+        self.forecasts_metadata = self.data_manager.get_data_metadata()
+        # Outputs data
+        self.y = {'time': a.array('d',[])}
+        for key in self.output_names:
+            # Do not store outputs that are current values of control inputs
+            flag = False
+            for key_u in self.input_names:
+                if key[:-2] == key_u[:-2]:
+                    flag = True
+                    break
+            if flag:
+                # Remove outputs that are current values of control inputs
+                # from outputs metadata dictionary
+                self.outputs_metadata.pop(key)
+            else:
+                self.y[key] = a.array('d',[])
+        self.y_store = copy.deepcopy(self.y)
+        # Inputs data
+        self.u = {'time':a.array('d',[])}
+        for key in self.input_names:
+            self.u[key] = a.array('d',[])
+        self.u_store = copy.deepcopy(self.u)
+
+    def __simulation(self,start_time,end_time,input_object=None):
+        '''Simulates the FMU using the pyfmi fmu.simulate function.
+
+        Parameters
+        ----------
+        start_time: int
+            Start time of simulation in seconds.
+        final_time: int
+            Final time of simulation in seconds.
+        input_object: pyfmi input_object, optional
+            Input object for simulation
+            Default is None
+
+        Returns
+        -------
+        res: pyfmi results object
+            Results of the fmu simulation.
+
+        '''
+
+        # Set fmu initialization option
+        self.options['initialize'] = self.initialize_fmu
+        # Set sample rate
+        step = end_time - start_time
+        if step >= 30:
+            self.options['ncp'] = int((end_time-start_time)/30)
+        elif step == 0:
+            pass
+        elif (step < 30) and (step > 0):
+            self.options['ncp'] = int((end_time-start_time)/step)
+        # Simulate fmu
+        try:
+            res = self.fmu.simulate(start_time=start_time,
+                                    final_time=end_time,
+                                    options=self.options,
+                                    input=input_object)
+        except:
+            return traceback.format_exc()
+        # Set internal fmu initialization
+        self.initialize_fmu = False
+
+        return res
+
+    def __get_results(self, res, store=True, store_initial=False):
+        '''Get results at the end of a simulation and throughout the
+        simulation period for storage. This method assigns these results
+        to `self.y` and, if `store=True`, also to `self.y_store` and
+        to `self.u_store`.
+        This method is used by `initialize()` and `advance()` to retrieve
+        results. `initialize()` does not store results whereas `advance()`
+        does.
+
+        Parameters
+        ----------
+        res: pyfmi results object
+            Results of the fmu simulation.
+        store: boolean
+            Set to true if desired to store results in `self.y_store` and
+            `self.u_store`
+        store_initial: boolean
+            Set to true if desired to store the initial point.
+
+        '''
+
+        # Determine if store initial point
+        if store_initial:
+            i = 0
+        else:
+            i = 1
+        # Store measurements
+        for key in self.y.keys():
+            self.y[key] = res[key][-1]
+            if store:
+                # Handle initialization of cs fmu generating multiple points for the same time
+                if res['time'][0] == res['time'][-1]:
+                    self.y_store[key].append(res[key][-1])
+                else:
+                    for x in res[key][i:]:
+                        self.y_store[key].append(x)
+        # Store control signals (will be baseline if not activated, test controller input if activated)
+        for key in self.u.keys():
+            # Replace '_u' and '_y' for key used to collect data and don't overwrite time
+            if key[-2:] == '_u':
+                key_data = key[:-2]+'_y'
+            elif key == 'time':
+                key_data = 'time'
+            else:
+                key_data = key
+            self.u[key] = res[key_data][-1]
+            if store:
+                # Handle initialization of cs fmu generating multiple points for the same time
+                if res['time'][0] == res['time'][-1]:
+                    self.u_store[key].append(res[key_data][-1])
+                else:
+                    for x in res[key_data][i:]:
+                        self.u_store[key].append(x)
+
+    def advance(self, u):
+        '''Advances the test case model simulation forward one step.
+
+        Parameters
+        ----------
+        u : dict
+            Defines the control input data to be used for the step.
+            {<input_name>_activate : bool, int, float, or str convertable to 1 or 0
+             <input_name>_u        : int or float, or str convertable to float}
+
+        Returns
+        -------
+        status: int
+            Indicates whether an advance request has been completed.
+            If 200, simulation advance was completed.
+            If 400, invalid inputs (non-numeric) were identified.
+            If 500, a simulation error occurred.
+        message: str
+            Includes the debug information
+        payload: dict
+            Contains the full state of measurement and input data at the end
+            of the step.
+            {<point_name> : <point_value>}
+            If empty, simulation end time has been reached.
+            If None, a simulation error has occurred.
+        '''
+
+        status = 200
+        # Calculate and store the elapsed time
+        if hasattr(self, 'tic_time'):
+            self.tac_time = time.time()
+            self.elapsed_control_time_ratio = np.append(self.elapsed_control_time_ratio, (self.tac_time-self.tic_time)/self.step)
+
+        # Set final time
+        self.final_time = self.start_time + self.step
+        alert_message = ''
+        # Set control inputs if they exist and are written
+        # Check if possible to overwrite
+        if u.keys():
+            # If there are overwriting keys available
+            # Check that any are overwritten
+            written = False
+            for key in u.keys():
+                if u[key]:
+                    written = True
+                    break
+            # If there are, create input object
+            if written:
+                u_list = []
+                u_trajectory = self.start_time
+                for key in u.keys():
+                    if (key not in self.input_names):
+                        payload = None
+                        status = 400
+                        message = "Unexpected input variable: {}.".format(key)
+                        logging.error(message)
+                        return status, message, payload
+                    if (key != 'time' and (u[key] != None)):
+                        if '_activate' in key:
+                            try:
+                                if float(u[key]) == 1:
+                                    checked_value = 1
+                                elif  float(u[key]) == 0:
+                                    checked_value = 0
+                                else:
+                                    payload = None
+                                    status = 400
+                                    message = "Invalid value {} and/or type {} for input {}. Input must be a boolean, float, integer, string, or unicode able to be converted to a 1 or 0 (or 'T[t]rue' or 'F[f]alse').".format(u[key], type(u[key]), key)
+                                    logging.error(message)
+                                    return status, message, payload
+                            except ValueError:
+                                if (u[key] == 'True') or (u[key] == 'true'):
+                                    checked_value = 1
+                                elif  (u[key] == 'False') or (u[key] == 'false'):
+                                    checked_value = 0
+                                else:
+                                    payload = None
+                                    status = 400
+                                    message = "Invalid value {} and/or type {} for input {}. Input must be a boolean, float, integer, string, or unicode able to be converted to a 1 or 0 (or 'T[t]rue' or 'F[f]alse').".format(u[key], type(u[key]), key)
+                                    logging.error(message)
+                                    return status, message, payload
+                        else:
+                            try:
+                                value = float(u[key])
+                            except:
+                                payload = None
+                                status = 400
+                                message = "Invalid value {} for input {}. Value must be a float, integer, or string able to be converted to a float, but is {}.".format(u[key], key, type(u[key]))
+                                logging.error(message)
+                                return status, message, payload
+                            # Check min/max if not activation input
+                            checked_value, message = self._check_value_min_max(key, value)
+                            if message is not None:
+                                logging.warning(message)
+                                alert_message = message + ';' + alert_message
+                        u_list.append(key)
+                        u_trajectory = np.vstack((u_trajectory, checked_value))
+                input_object = (u_list, np.transpose(u_trajectory))
+            # Otherwise, input object is None
+            else:
+                input_object = None
+        # Otherwise, input object is None
+        else:
+            input_object = None
+        # Simulate if not end of test
+        if self.start_time < self.end_time:
+            # Make sure stop at end of test
+            if self.final_time > self.end_time:
+                self.final_time = self.end_time
+            res = self.__simulation(self.start_time, self.final_time, input_object)
+            # Process results
+            if not isinstance(res, str):
+                # Get result and store measurement and control inputs
+                self.__get_results(res, store=True, store_initial=False)
+                # Raise the flag to compute time lapse
+                self.tic_time = time.time()
+                # Get full current state
+                payload = self._get_full_current_state()
+                # Write any messages
+                if alert_message == '':
+                    message = "Advanced simulation successfully from {0}s to {1}s.".format(self.start_time, self.final_time)
+                else:
+                    message = alert_message
+                # Advance start time
+                self.start_time = self.final_time
+                # Check if scenario is over
+                if self.start_time >= self.end_time:
+                    self.scenario_end = True
+                # Log and return
+                logging.info(message)
+                return status, message, payload
+
+            else:
+                # Errors in the simulation
+                status = 500
+                message = "Failed to advance simulation: {}.".format(res)
+                payload = res
+                logging.error(message)
+                return status, message, payload
+        else:
+            # Simulation at end time
+            payload = dict()
+            message = "End of test case scenario time period reached."
+            logging.info(message)
+
+            return status, message, payload
+
+    def initialize(self, start_time, warmup_period, end_time=np.inf):
+        '''Initialize the test simulation.
+
+        Parameters
+        ----------
+        start_time: int or float
+            Start time of simulation to initialize to in seconds.
+        warmup_period: int or float
+            Length of time before start_time to simulate for warmup in seconds.
+        end_time: int or float, optional
+            Specifies a finite end time to allow the simulation to continue
+            Default value is infinite.
+
+        Returns
+        -------
+        status: int
+            Indicates whether an initialization request has been completed.
+            If 200, initialization was completed successfully.
+            If 400, an invalid start time or warmup period (non-numeric) was identified.
+            If 500, an error occurred during the initialization simulation.
+        message: str
+            Includes detailed debugging information.
+        payload: dict
+            Contains the full state of measurement and input data at the end
+            of the initialization period.
+            {<point_name> : <point_value>}.
+            If None, an error occurred during the initialization simulation.
+
+        '''
+
+        status = 200
+        payload = None
+        # Reset fmu
+        self.fmu.reset()
+        # Reset simulation data storage
+        self.__initilize_data()
+        self.elapsed_control_time_ratio = np.array([])
+        # Check if the inputs are valid
+        try:
+            start_time = float(start_time)
+        except:
+            payload = None
+            status = 400
+            message = "Invalid value {} for parameter start_time. Value must be a float, integer, or string able to be converted to a float, but is {}.".format(start_time, type(start_time))
+            logging.error(message)
+            return status, message, payload
+        try:
+            warmup_period = float(warmup_period)
+        except:
+            payload = None
+            status = 400
+            message = "Invalid value {} for parameter warmup_period. Value must be a float, integer, or string able to be converted to a float, but is {}.".format(warmup_period, type(warmup_period))
+            logging.error(message)
+            return status, message, payload
+        if start_time < 0:
+            payload = None
+            status = 400
+            message = "Invalid value {} for parameter start_time. Value must not be negative.".format(start_time)
+            logging.error(message)
+            return status, message, payload
+        if warmup_period < 0:
+            payload = None
+            status = 400
+            message = "Invalid value {} for parameter warmup_period. Value must not be negative.".format(warmup_period)
+            logging.error(message)
+            return status, message, payload
+        # Record initial testing time
+        self.initial_time = start_time
+        # Record end testing time
+        self.end_time = end_time
+        # Set fmu intitialization
+        self.initialize_fmu = True
+        # Simulate fmu for warmup period.
+        # Do not allow negative starting time to avoid confusions
+        res = self.__simulation(max(start_time-warmup_period, 0), start_time)
+        # Process result
+        if not isinstance(res, str):
+            # Get result
+            self.__get_results(res, store=True, store_initial=True)
+            # Set internal start time to start_time
+            self.start_time = start_time
+            # Initialize KPI Calculator
+            self.cal.initialize()
+            # Set scenario end flag to false
+            self.scenario_end = False
+            # Get full current state
+            payload = self._get_full_current_state()
+            message = "Test simulation initialized successfully to {0}s with warmup period of {1}s.".format(self.start_time, warmup_period)
+            logging.info(message)
+
+            return status, message, payload
+
+        else:
+            payload = None
+            status = 500
+            message = "Failed to initialize test simulation: {}.".format(res)
+            logging.error(message)
+
+            return status, message, payload
+
+    def get_step(self):
+        '''Returns the current control step in seconds.
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        status: int
+            Indicates whether a request for querying the control step has been completed.
+            If 200, the step was successfully queried.
+            If 500, an internal error occurred.
+        message: str
+            Includes detailed debugging information.
+        payload: int
+            The current control step.
+            None if error during query.
+
+        '''
+
+        status = 200
+        message = "Queried the control step successfully."
+        payload = None
+        try:
+            payload = self.step
+            logging.info(message)
+        except:
+            status = 500
+            message = "Failed to query the simulation step: {}".format(traceback.format_exc())
+            logging.error(message)
+
+        return status, message, payload
+
+    def set_step(self, step):
+        '''Sets the control step in seconds.
+
+        Parameters
+        ----------
+        step: int or float
+            Control step in seconds.
+
+        Returns
+        -------
+        status: int
+            Indicates whether a request for setting the control step has been completed.
+            If 200, the step was successfully set.
+            If 400, an invalid simulation step (non-numeric) was identified.
+            If 500, an internal error occurred.
+        message: str
+            Includes detailed debugging information.
+        payload:
+            None
+
+        '''
+
+        status = 200
+        message = "Control step set successfully."
+        payload = None
+        try:
+            step = float(step)
+        except:
+            payload = None
+            status = 400
+            message = "Invalid value {} for parameter step. Value must be a float, integer, or string able to be converted to a float, but is {}.".format(step, type(step))
+            logging.error(message)
+            return status, message, payload
+        if step < 0:
+            payload = None
+            status = 400
+            message = "Invalid value {} for parameter step. Value must not be negative.".format(step)
+            logging.error(message)
+            return status, message, payload
+        try:
+            self.step = step
+        except:
+            payload = None
+            status = 500
+            message = "Failed to set the control step: {}".format(traceback.format_exc())
+            logging.error(message)
+            return status, message, payload
+        payload={'step':self.step}
+        logging.info(message)
+
+        return status, message, payload
+
+    def get_inputs(self):
+        '''Returns a dictionary of control inputs and their meta-data.
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        status: int
+            Indicates whether a request for querying the inputs has been completed.
+            If 200, the inputs were successfully queried.
+            If 500, an internal error occurred.
+        message: str
+            Includes detailed debugging information.
+        payload: dict
+            Dictionary of control inputs and their meta-data.
+            Returns None if error in getting inputs and meta-data.
+
+        '''
+
+        status = 200
+        message = "Queried the inputs successfully."
+        payload = None
+        try:
+            payload = self.inputs_metadata
+            logging.info(message)
+        except:
+            status = 500
+            message = "Failed to query the input list: {}".format(traceback.format_exc())
+            logging.error(message)
+
+        return status, message, payload
+
+    def get_measurements(self):
+        '''Returns a dictionary of measurements and their meta-data.
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        status: int
+            Indicates whether a request for querying the outputs has been completed.
+            If 200, the outputs were successfully queried.
+            If 500, an internal error occurred.
+        message: str
+            Includes detailed debugging information.
+        payload : dict
+            Dictionary of measurements and their meta-data.
+            Returns None if error in getting measurements and meta-data.
+
+        '''
+
+        status = 200
+        message = "Queried the measurements successfully."
+        payload = None
+        try:
+            payload = self.outputs_metadata
+            logging.info(message)
+        except:
+            status = 500
+            message = "Failed to query the measurement list: {}".format(traceback.format_exc())
+            logging.error(message)
+
+        return status, message, payload
+
+    def get_results(self, point_names, start_time, final_time):
+        '''Returns measurement and control input trajectories.
+
+        Parameters
+        ----------
+        point_names: list
+            Variable names.
+        start_time : int or float
+            Start time of data to return in seconds.
+        final_time : int or float
+            Start time of data to return in seconds.
+
+        Returns
+        -------
+        status: int
+            Indicates whether a request for querying the results has been completed.
+            If 200, the results were successfully queried.
+            If 400, invalid start time and/or invalid final time (non-numeric) were identified.
+            If 500, an internal error occured.
+        message: str
+            Includes detailed debugging information.
+        payload : dict
+            Dictionary of variable trajectories with time as lists.
+            {'time':[<time_data>],
+             'var':[<var_data>]
+            }
+            Returns None if no variable can be found or a simulation error occurs.
+
+        '''
+
+        status = 200
+        try:
+            start_time = float(start_time)
+        except:
+            payload = None
+            status = 400
+            message = "Invalid value {} for parameter start_time. Value must be a float, integer, or string able to be converted to a float, but is {}.".format(start_time, type(start_time))
+            logging.error(message)
+            return status, message, payload
+        try:
+            final_time = float(final_time)
+        except:
+            payload = None
+            status = 400
+            message = "Invalid value {} for parameter final_time. Value must be a float, integer, or string able to be converted to a float, but is {}.".format(final_time, type(final_time))
+            logging.error(message)
+            return status, message, payload
+        payload = {}
+        try:
+            for point_name in point_names:
+                # Get correct points
+                if point_name in self.y_store.keys():
+                    payload[point_name] = self.y_store[point_name]
+                elif point_name in self.u_store.keys():
+                    payload[point_name] = self.u_store[point_name]
+                else:
+                    status = 400
+                    message = "Invalid point name {} in parameter point_names.  Check lists of available inputs and measurements.".format(point_name)
+                    logging.error(message)
+                    return status, message, None
+            if any(item in point_names for item in self.y_store.keys()):
+                payload['time'] = self.y_store['time']
+            elif any(item in point_names for item in self.u_store.keys()):
+                payload['time'] = self.u_store['time']
+            # Get correct time
+            if payload and 'time' in payload:
+                # Find min and max time
+                min_t = min(payload['time'])
+                max_t = max(payload['time'])
+                # If min time is before start time
+                if min_t < start_time:
+                    # Check if start time in time array
+                    if start_time in payload['time']:
+                        t1 = start_time
+                    # Otherwise, find first time in time array after start time
+                    else:
+                        np_t = np.array(payload['time'])
+                        t1 = np_t[np_t>=start_time][0]
+                # Otherwise, first time is min time
+                else:
+                    t1 = min_t
+                # If max time is after final time
+                if max_t > final_time:
+                    # Check if final time in time array
+                    if final_time in payload['time']:
+                        t2 = final_time
+                    # Otherwise, find last time in time array before final time
+                    else:
+                        np_t = np.array(payload['time'])
+                        t2 = np_t[np_t<=final_time][-1]
+                # Otherwise, last time is max time
+                else:
+                    t2 = max_t
+                # Use found first and last time to find corresponding indecies
+                i1 = payload['time'].index(t1)
+                i2 = payload['time'].index(t2)+1
+                for key in (point_names +['time']):
+                    payload[key] = payload[key][i1:i2]
+        except:
+            status = 500
+            message = "Failed to query simulation results: {}".format(traceback.format_exc())
+            logging.error(message)
+            return status, message, None
+
+        if not isinstance(payload, (list, type(None))):
+            for key in payload:
+                payload[key] = payload[key].tolist()
+
+        message = "Queried results data successfully for point names {}.".format(point_names)
+        logging.info(message)
+        return status, message, payload
+
+    def get_kpis(self):
+        '''Returns KPI data.
+
+        Requires standard sensor signals.
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        status: int
+            Indicates whether a request for querying the KPIs has been completed.
+            If 200, the KPIs were successfully queried.
+            If 500, an internal error occured.
+        message: str
+            Includes detailed debugging information
+        payload : dict
+            Dictionary containing KPI names and values.
+            {<kpi_name>:<kpi_value>}.
+            Returns None if error during calculation.
+
+        '''
+
+        status = 200
+        message = "Queried KPIs successfully."
+        try:
+            # Set correct price scenario for cost
+            if self.scenario['electricity_price'] == 'constant':
+                price_scenario = 'Constant'
+            elif self.scenario['electricity_price'] == 'dynamic':
+                price_scenario = 'Dynamic'
+            elif self.scenario['electricity_price'] == 'highly_dynamic':
+                price_scenario = 'HighlyDynamic'
+            # Calculate the core kpis
+            payload = self.cal.get_core_kpis(price_scenario=price_scenario)
+        except:
+            payload = None
+            status = 500
+            message = "Failed to query KPIs: {}".format(traceback.format_exc())
+            logging.error(message)
+        logging.info(message)
+
+        return status, message, payload
+
+    def get_forecast_points(self):
+        '''Returns a dictionary of available forecast points and their meta-data.
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        status: int
+            Indicates whether a request for querying the forecast points has been completed.
+            If 200, the outputs were successfully queried.
+            If 500, an internal error occurred.
+        message: str
+            Includes detailed debugging information.
+        payload : dict
+            Dictionary of forecast points and their meta-data.
+            Returns None if error in getting forecast points and meta-data.
+
+        '''
+
+        # Get the forecast
+        status = 200
+        message = "Queried the forecast points and their meta-data successfully."
+        try:
+            payload = self.forecasts_metadata
+        except:
+            status = 500
+            message = "Failed to query the test case forecast points and their meta-data: {}".format(traceback.format_exc())
+            payload = None
+            logging.error(message)
+        logging.info(message)
+
+        return status, message, payload
+
+    def get_forecast(self, point_names, horizon, interval, temperature_uncertainty=None, solar_uncertainty=None):
+        '''Returns the test case data forecast
+
+        Parameters
+        ----------
+        point_names : list of str
+            List of forecast point names for which to get data.
+        horizon: int or float
+            Forecast horizon in seconds.
+        interval: int or float
+            Forecast interval in seconds.
+
+        Returns
+        -------
+        status: int
+            Indicates whether a request for querying the forecast has been successfully completed.
+            If 200, the forecast was successfully queried.
+            If 500, an internal error occurred.
+        message: str
+            Includes detailed debugging information
+        payload: dict
+            Dictionary with the requested forecast data
+            {<variable_name>:<variable_forecast_trajectory>}
+            where <variable_name> is a string with the variable
+            key and <variable_forecast_trajectory> is a list with
+            the forecasted values. 'time' is included as a variable.
+
+        '''
+
+
+        # Get the forecast
+        status = 200
+        message = "Queried the forecast data successfully."
+        if 'temperature_uncertainty' in self.scenario or 'solar_uncertainty' in self.scenario:
+            if (self.scenario.get('temperature_uncertainty') and temperature_uncertainty) or (self.scenario.get('solar_uncertainty') and solar_uncertainty):
+                payload = None
+                status = 400
+
+                message = "The scenario configuration already includes specific uncertainty settings. "
+                if (self.scenario.get('temperature_uncertainty') and temperature_uncertainty):
+                    message += "'temperature_uncertainty' is already set to '{}', further overriding of 'temperature_uncertainty' is not allowed. ".format(
+                        self.scenario['temperature_uncertainty'])
+                if (self.scenario.get('solar_uncertainty') and solar_uncertainty):
+                    message += "'solar_uncertainty' is already set to '{}', further overriding of 'solar_uncertainty' is not allowed. ".format(
+                        self.scenario['solar_uncertainty'])
+        # if 'solar_uncertainty' in self.scenario:
+        #     if (self.scenario['solar_uncertainty'] and solar_uncertainty):
+
+
+
+                logging.error(message)
+                return status, message, payload
+
+            if self.scenario.get('temperature_uncertainty'):
+                temperature_uncertainty=self.scenario['temperature_uncertainty']
+            if self.scenario.get('solar_uncertainty'):
+                solar_uncertainty=self.scenario['solar_uncertainty']
+
+        # Check inputs
+        try:
+            horizon = float(horizon)
+        except:
+            payload = None
+            status = 400
+            message = "Invalid value {} for parameter horizon. Value must be a float, integer, or string able to be converted to a float, but is {}.".format(horizon, type(horizon))
+            logging.error(message)
+            return status, message, payload
+        try:
+            interval = float(interval)
+        except:
+            payload = None
+            status = 400
+            message = "Invalid value {} for parameter interval. Value must be a float, integer, or string able to be converted to a float, but is {}.".format(interval, type(interval))
+            logging.error(message)
+            return status, message, payload
+
+        if interval <= 0:
+            payload = None
+            status = 400
+            message = "Invalid value {} for parameter interval. Value must be positive.".format(interval)
+            logging.error(message)
+            return status, message, payload
+            # Check the temperature_uncertainty value
+        # if temperature_uncertainty and 'TDryBul' not in point_names:
+        #     payload = None
+        #     status = 400
+        #     message = "Temperature uncertainty provided but 'TDryBul' is missing in point_names."
+        #     logging.error(message)
+        #     return status, message, payload
+
+        # if solar_uncertainty and 'HGloHor' not in point_names:
+        #     payload = None
+        #     status = 400
+        #     message = "Solar uncertainty provided but 'HGloHor' is missing in point_names."
+        #     logging.error(message)
+        #     return status, message, payload
+        allowed_uncertainties = [None, 'low', 'medium', 'high']
+        if temperature_uncertainty not in allowed_uncertainties:
+            payload = None
+            status = 400
+            message = "Invalid value for temperature_uncertainty. Allowed values are: {}".format(
+                allowed_uncertainties[1:])
+            logging.error(message)
+            return status, message, payload
+
+        # Check the solar_uncertainty value
+        if solar_uncertainty not in allowed_uncertainties:
+            payload = None
+            status = 400
+            message = "Invalid value for solar_uncertainty. Allowed values are: {}".format(allowed_uncertainties[1:])
+            logging.error(message)
+            return status, message, payload
+
+        wrong_points = []
+        for point in point_names:
+            if point not in self.forecast_names:
+                wrong_points.append(str(point))
+        if wrong_points:
+            payload = None
+            status = 400
+            message = "Invalid point name(s) {} in parameter point_names.  Check list of available forecast points.".format(wrong_points)
+            logging.error(message)
+            return status, message, payload
+        # Check for missing point_names related to uncertainty parameters
+
+        temperature_params = {
+            "F0": 0, "K0": 0, "F": 0, "K": 0, "mu": 0
+        }
+
+        solar_params = {
+            "ag0": 0, "bg0": 0, "phi": 0, "ag": 0, "bg": 0
+        }
+
+        if temperature_uncertainty is not None:
+            temperature_params.update(self.uncertainty_params['temperature'][temperature_uncertainty])
+
+        if solar_uncertainty is not None:
+            solar_params.update(self.uncertainty_params['solar'][solar_uncertainty])
+
+        try:
+
+            payload = self.forecaster.get_forecast(
+                point_names,
+                horizon=horizon,
+                interval=interval,
+                weather_temperature_dry_bulb=temperature_params,
+                weather_solar_global_horizontal=solar_params
+            )
+
+        except:
+            status = 500
+            message = "Failed to query the test case forecast data: {}".format(traceback.format_exc())
+            payload = None
+            logging.error(message)
+        logging.info(message)
+
+        return status, message, payload
+
+    def set_scenario(self, scenario):
+        '''Sets the test case scenario.
+
+        Parameters
+        ----------
+        scenario : dict
+            {'electricity_price': <'constant' or 'dynamic' or 'highly_dynamic'>,
+             'time_period': see available <str> keys for test case,
+             'temperature_uncertainty':<'low' or 'medium' or 'high'>,
+             'solar_uncertainty':<'low' or 'medium' or 'high'>
+            }
+            If any value is None, it will not change existing.
+
+        Returns
+        -------
+        status: int
+            Indicates whether a request for setting the scenario has been completed
+            If 200, the scenario was successfully set.
+            If 400, invalid electricity_price and/or time_period (non-numeric) were identified.
+            If 500, an internal error occurred.
+        message: str
+            Includes the detailed debug information
+        payload: dict
+            {'electricity_price': if succeeded in changing then True, else None,
+             'time_period': if succeeded then initial measurements, else None,
+             'temperature_uncertainty': if succeeded in changing then True, else None,
+             'solar_uncertainty': if succeeded in changing then True, else None
+            }
+        '''
+
+        status = 200
+        message = "Test case scenario was set successfully."
+        payload = {
+            'electricity_price': None,
+            'time_period': None,
+            'temperature_uncertainty': None,
+            'solar_uncertainty': None
+        }
+        if not hasattr(self, 'scenario'):
+            self.scenario = {}
+        try:
+            # Handle electricity price
+            if scenario['electricity_price']:
+                if scenario['electricity_price'] not in ['constant', 'dynamic', 'highly_dynamic']:
+                    status = 400
+                    message = "Scenario parameter electricy_price is {}, " \
+                              "but should be 'constant', 'dynamic', or 'highly_dynamic'.". \
+                              format(scenario['electricity_price'])
+                    logging.error(message)
+                    return status, message, payload
+                self.scenario['electricity_price'] = scenario['electricity_price']
+                payload['electricity_price'] = self.scenario['electricity_price']
+            # Handle timeperiod
+            if scenario['time_period']:
+                if scenario['time_period'] not in self.days_json:
+                    status = 400
+                    message = "Scenario parameter time_period is {}, but " \
+                              "should be one of the following: {}.". \
+                              format(scenario['time_period'], list(self.days_json.keys()))
+                    logging.error(message)
+                    return status, message, payload
+                self.scenario['time_period'] = scenario['time_period']
+                warmup_period = 7*24*3600.
+                key = self.scenario['time_period']
+                start_time = self.days_json[key]*24*3600.-7*24*3600.
+                end_time = start_time + 14*24*3600.
+
+            # Handle temperature uncertainty
+
+
+
+            if scenario['temperature_uncertainty']:
+                if scenario['temperature_uncertainty'] not in ['low', 'medium', 'high']:
+                    status = 400
+                    message = "Scenario parameter temperature_uncertainty is {}, " \
+                              "but should be 'low', 'medium' or 'high'.". \
+                              format(scenario['temperature_uncertainty'])
+                    logging.error(message)
+                    return status, message, payload
+                self.scenario['temperature_uncertainty'] = scenario['temperature_uncertainty']
+                payload['temperature_uncertainty'] = self.scenario['temperature_uncertainty']
+            else:
+                self.scenario['temperature_uncertainty'] = None
+
+            # Handle solar uncertainty
+            if scenario['solar_uncertainty']:
+                if scenario['solar_uncertainty'] not in ['low', 'medium', 'high']:
+                    status = 400
+                    message = "Scenario parameter solar_uncertainty is {}, " \
+                              "but should be 'low', 'medium' or 'high'.". \
+                        format(scenario['solar_uncertainty'])
+                    logging.error(message)
+                    return status, message, payload
+                self.scenario['solar_uncertainty'] = scenario['solar_uncertainty']
+                payload['solar_uncertainty'] = self.scenario['solar_uncertainty']
+            else:
+                self.scenario['solar_uncertainty'] = None
+
+        except:
+            status = 400
+            message = "Invalid values for the scenario parameters: {}".format(traceback.format_exc())
+            logging.error(message)
+            return status, message, payload
+        try:
+            if scenario['time_period']:
+                initialize_payload = self.initialize(start_time, warmup_period, end_time=end_time)
+                if initialize_payload[0] != 200:
+                    status = 500
+                    message = initialize_payload[1]
+                    logging.error(message)
+                    return status, message, payload
+                payload['time_period'] = initialize_payload[2]
+            # It's needed to reset KPI Calculator when scenario is changed
+            self.cal.initialize()
+        except:
+            status = 500
+            message = "Failed to set the test case scenario: {}".format(traceback.format_exc())
+            payload = None
+            logging.error(message)
+        logging.info(message)
+
+        return status, message, payload
+
+    def get_scenario(self):
+        '''Returns the current case scenario.
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        status: int
+            Indicates whether a request for querying the scenario has been successfully completed
+            If 200, the scenario was successfully queried.
+            If 500, an internal error occurred.
+        message: str
+            Includes detailed debugging information
+        payload: dict
+            {'electricity_price': <str>,
+             'time_period': <str>
+             }
+
+        '''
+
+        payload = None
+        status = 200
+        message = "Queried current test case for scenario successfully."
+        try:
+            payload = self.scenario
+        except:
+            status = 500
+            message = "Failed to find a current test case scenario setting.  Check it was set properly: {}".format(traceback.format_exc())
+            logging.error(message)
+        logging.info(message)
+
+        return status, message, payload
+
+    def get_name(self):
+        '''Returns the name of the test case fmu.
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        status: int
+            Indicate whether a request for querying the name of the test case has been successfully completed.
+            If 200, the name was successfully queried.
+            If 500, an internal error occurred.
+        message: str
+            Includes detailed debugging information
+        payload :  dict
+            Name of test case as {'name': <str>}
+
+        '''
+
+        status = 200
+        message = "Queried the name of the test case successfully."
+        payload = {'name': self.name}
+        logging.info(message)
+
+        return status, message, payload
+
+    def get_version(self):
+        '''Returns the version number of BOPTEST.
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        status: int
+            Indicate whether a request for querying the version number of BOPTEST has been completed.
+            If 200, the version was successfully queried.
+            If 500, an internal error occurred.
+        message: str
+            Includes detailed debugging information
+        payload:  dict
+            Version of BOPTEST as {'version': <str>}
+
+        '''
+
+        status = 200
+        message = "Queried the version number successfully."
+        logging.info(message)
+        payload = {'version': self.version}
+
+        return status, message, payload
+
+    def post_results_to_dashboard(self, api_key, tags, unit_test=False):
+        '''Posts test results to online dashboard at given server address.
+
+        Parameters
+        ----------
+        api_key : str
+            API key corresponding to user account for dashboard.
+        tags : list of str
+            List of tags to be included with result posting.
+        unit_test : bool
+            True if API being called for unit testing so as not to post to online dashboard.
+
+        Returns
+        -------
+        status: int
+            Indicates whether a posting to the dashboard has been successfully completed.
+            If 200, the posting was successful.
+            If 400, there was an input error.
+            If 500, an internal error occurred.
+        message: str
+            Includes detailed debugging information
+        payload: None
+
+        '''
+
+        # Check formal scnenario test completed
+        if not self.scenario_end:
+            status = 500
+            message = 'Formal test scenario, including time period, has not been completed.  Initialize a test scenario using the PUT /scenario API and run it to completion before submitting results to dashboard.'
+            logging.error(message)
+            return status, message, None
+        # Check parameters
+        if not isinstance(api_key, str):
+            status = 400
+            message = 'Invalid type for input api_key. It must be a string, but is a {0}.'.format(type(api_key))
+            logging.error(message)
+            return status, message, None
+
+        if not isinstance(tags, list):
+            status = 400
+            message = 'Invalid type for input tags. It must be a list of strings, but is a {0}.'.format(type(tags))
+            logging.error(message)
+            return status, message, None
+
+        if len(tags)>10:
+            status = 400
+            message = 'Invalid number of tags. The limit is 10, but there are {0}.'.format(len(tags))
+            logging.error(message)
+            return status, message, None
+
+        for tag in tags:
+            if not isinstance(tag, str):
+                status = 400
+                message = 'Invalid type for one of the tag inputs. They must be strings, but one is a {0}.'.format(type(tag))
+                logging.error(message)
+                return status, message, None
+        # Specify server address and payload
+        dash_server = os.environ['BOPTEST_DASHBOARD_SERVER']
+        # Create payload
+        uid = str(uuid.uuid4())
+        payload = {
+          "results": [
+            {
+              "uid": uid,
+              "dateRun": str(datetime.now(tz=pytz.UTC)),
+              "boptestVersion": self.version,
+              "isShared": True,
+              "controlStep": str(self.get_step()[2]),
+              "account": {
+                "apiKey": api_key
+              },
+              "forecastParameters":{},
+              "tags": tags,
+              "kpis": self.get_kpis()[2],
+              "scenario": self.add_forecast_uncertainty(self.keys_to_camel_case(self.get_scenario()[2])),
+              "buildingType": {
+                "uid": self.get_name()[2]['name']
+              }
+            }
+          ]
+        }
+        dash_url = "%s/api/results" % dash_server
+        # Post to dashboard
+        if not unit_test:
+            result = requests.post(dash_url, json=payload)
+        else:
+            message = '/submit API run in unit test mode'
+            logging.info(message)
+            status = 200
+            payload_ret = {'dash_url':dash_url, 'payload':payload}
+            return status, message, payload_ret
+
+        # Interpret response
+        status = result.status_code
+        if status == 200:
+            message = 'Results submitted successfully to dashboard at {0}.'.format(dash_server)
+            logging.info(message)
+            payload = {'identifier':uid}
+        else:
+            if 'Could not find any entity of type "accounts" matching' in result.json()['rejected'][0]['message']:
+                message = 'Error submitting results to dashboard at {0}. Check the dashboard user account API key is correct. Full dashboard response is: {1}.'.format(dash_server, result.json())
+            else:
+                message = 'Error submitting results to dashboard at {0}. Full dashboard response is: {1}.'.format(dash_server, result.json())
+            logging.error(message)
+            payload = None
+
+        return status, message, payload
+
+    def _get_elapsed_control_time_ratio(self):
+        '''Returns the elapsed control time ratio vector for the case.
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        elapsed_control_time_ratio : np array of floats
+            elapsed_control_time_ratio for each control step.
+
+        '''
+
+        elapsed_control_time_ratio = self.elapsed_control_time_ratio
+
+        return elapsed_control_time_ratio
+
+    def _get_var_metadata(self, fmu, var_list, inputs=False):
+        '''Build a dictionary of variables and their metadata.
+
+        Parameters
+        ----------
+        fmu : pyfmi fmu object
+            FMU from which to get variable metadata
+        var_list : list of str
+            List of variable names
+
+        Returns
+        -------
+        var_metadata : dict
+            Dictionary of variable names as keys and metadata as fields.
+            {<var_name_str> :
+                "Unit" : str,
+                "Description" : str,
+                "Minimum" : float,
+                "Maximum" : float
+            }
+
+        '''
+
+        # Inititalize
+        var_metadata = dict()
+        # Get metadata
+        for var in var_list:
+            # Units
+            if var == 'time':
+                unit = 's'
+                description = 'Time of simulation'
+                mini = None
+                maxi = None
+            elif '_activate' in var:
+                unit = None
+                description = fmu.get_variable_description(var)
+                mini = None
+                maxi = None
+            else:
+                unit = fmu.get_variable_unit(var)
+                description = fmu.get_variable_description(var)
+                if inputs:
+                    mini = fmu.get_variable_min(var)
+                    maxi = fmu.get_variable_max(var)
+                else:
+                    mini = None
+                    maxi = None
+            var_metadata[var] = {'Unit':unit,
+                                 'Description':description,
+                                 'Minimum':mini,
+                                 'Maximum':maxi}
+
+        return var_metadata
+
+    def _check_value_min_max(self, var, value):
+        '''Check that the input value does not violate the min or max.
+
+        Note that if it does, the value is truncated to the minimum or maximum.
+
+        Parameters
+        ----------
+        var : str
+            Name of variable
+        value : numeric
+            Specified value of variable
+
+        Return
+        ------
+        checked_value : float
+            Value of variable truncated by min and max.
+        message: str
+            Alert messages that input value is truncated to min/max
+        '''
+
+        # Get minimum and maximum for variable
+        mini = self.inputs_metadata[var]['Minimum']
+        maxi = self.inputs_metadata[var]['Maximum']
+        message = None
+        # Check the value and truncate if necessary
+        if value > maxi:
+            checked_value = maxi
+            message = 'WARNING: value of {0} for {1} is above maximum of {2}.  Using {2}. '.format(value, var, maxi)
+        elif value < mini:
+            checked_value = mini
+            message = 'WARNING: value of {0} for {1} is below minimum of {2}.  Using {2}. '.format(value, var, mini)
+        else:
+            checked_value = value
+
+        return checked_value, message
+
+    def _get_area(self):
+        '''Get the building floor area in m^2.
+
+        Returns
+        -------
+        area: float
+            Building floor area in m^2
+
+        '''
+
+        area = self.area
+
+        return area
+
+    def _get_full_current_state(self):
+        '''Combines the self.y and self.u dictionaries into one.
+
+        Returns
+        -------
+        z: dict
+            Combination of self.y and self.u dictionaries.
+
+        '''
+
+        z = self.y.copy()
+        z.update(self.u)
+
+        return z
+
+    def to_camel_case(self, snake_str):
+        components = snake_str.split('_')
+        # We capitalize the first letter of each component except the first one
+        # with the 'title' method and join them together.
+        return components[0] + ''.join(x.title() for x in components[1:])
+
+    def keys_to_camel_case(self, a_dict):
+        result = {}
+        for key, value in a_dict.items():
+            result[self.to_camel_case(key)] = value
+        return result
+
+    # weatherForecastUncertainty is required by the dashboard,
+    # however some testcases don't report it.
+    # This is a workaround
+    def add_forecast_uncertainty(self, scenario):
+        if not 'weatherForecastUncertainty' in scenario:
+            scenario['weatherForecastUncertainty'] = 'deterministic'
+
+        return scenario
+
+    def load_uncertainty_params(self, filepath='forecast/forecast_uncertainty_params.json'):
+        '''Load the uncertainty parameters from a JSON file.
+
+        Parameters
+        ----------
+        filepath : str, optional
+            Path to the JSON file containing the uncertainty parameters.
+            Default is 'forecast_uncertainty_params.json'.
+
+        Returns
+        -------
+        dict
+            Uncertainty parameters loaded from the JSON file.
+        '''
+        with open(filepath, 'r') as f:
+            uncertainty_params = json.load(f)
+        return uncertainty_params
\ No newline at end of file
diff --git a/testing/testcase3/version.txt b/testing/testcase3/version.txt
new file mode 100644
index 000000000..4259c642a
--- /dev/null
+++ b/testing/testcase3/version.txt
@@ -0,0 +1 @@
+0.5.0-dev
diff --git a/testing/utilities.py b/testing/utilities.py
index 639cfc6c9..e8ec4c0b9 100644
--- a/testing/utilities.py
+++ b/testing/utilities.py
@@ -14,7 +14,7 @@
 import re
 import matplotlib.pyplot as plt
 from datetime import datetime
-
+import sys
 
 def get_root_path():
     '''Returns the path to the root repository directory.
@@ -77,7 +77,6 @@ def run_tests(test_file_name, test_names=[]):
     with open(os.path.join(get_root_path(),'testing',log_file), 'w') as f:
         json.dump(log_json, f)
 
-
 def compare_references(vars_timeseries = ['reaTRoo_y'],
                        refs_old = 'multizone_residential_hydronic_old',
                        refs_new = 'multizone_residential_hydronic'):
@@ -1044,3 +1043,29 @@ def run_season(self, season):
             df.index.name = 'keys'
             ref_filepath = os.path.join(get_root_path(), 'testing', 'references', self.name, 'kpis_{0}_{1}.csv'.format(season, price_scenario))
             self.compare_ref_values_df(df, ref_filepath)
+import statsmodels.api as sm
+from scipy.stats import laplace
+def check_params_gaussain(errors):
+    F0 = errors[:, 0].mean()
+    K0 = errors[:, 0].std()
+
+    X = errors[:, :-1].reshape((-1, 1))
+    y = errors[:, 1:].reshape((-1, 1))
+    model = sm.OLS(y, X, missing='drop').fit()
+    F = model.params.item()
+    K = model.resid.std()
+    mu = model.resid.mean()
+    return (F0,K0,F,K,mu)
+
+
+def check_params_laplace(errors):
+    ag0,bg0=laplace.fit(errors[:, 0])
+
+    X = errors[:, :-1].reshape((-1, 1))
+    y = errors[:, 1:].reshape((-1, 1))
+    model = sm.OLS(y, X, missing='drop').fit()
+    phi = model.params.item()
+    h=model.resid
+    ag, bg = laplace.fit(h)
+    return (ag0, bg0, phi, ag, bg)
+
diff --git "a/\357\274\201" "b/\357\274\201"
new file mode 100644
index 000000000..fbf0d0198
--- /dev/null
+++ "b/\357\274\201"
@@ -0,0 +1,10 @@
+{
+  "registry-mirrors": [
+    "https://xx4bwyg2.mirror.aliyuncs.com",
+    "http://f1361db2.m.daocloud.io",
+    "https://registry.docker-cn.com",
+    "http://hub-mirror.c.163.com",
+    "https://docker.mirrors.ustc.edu.cn"
+  ]
+}{}
+