Our interdisciplinary research group aims to bridge the gap between basic and applied research. One of the main focuses of the group is the application of computing technologies and AI to the design and optimization of intelligent energy systems, with the goal of developing more efficient, sustainable, and resilient energy infrastructure. Our research themes include
- Computational Methods and Machine Learning
- IoT Technologies for Intelligent Systems
- User-centered Services
Gerald's thoughts on science, funding, and excellence.
Our Paper "Constructing neural network-based models for simulating dynamical systems" got accepted in ACM Computing Surveys.
Our Paper "Learning Non-linear White-box Predictors" got accepted at IEEE Conference on ML and Applications (IEEE ICMLA)
Our Paper "Deep learning-based bias adjustment of decadal climate predictions" got accepted at NeurIPS 2022 Workshop.
Our Paper "An Open IoT Platform: Lessons Learned from a District Energy System" got accepted at IEEE SMART22.
Our Paper "A Dymola-Python framework for data-driven model creation and co-simulation" got accepted at Modelica Conf. 2022.
Our Paper "IoT Middleware Platforms for Smart Energy Systems" got accepted in Buildings.
The project Ecom4Future start in January 2024. The project Autology start in November 2023. The project PersonAI start in November 2023.
- Video on User-centered Energy Services
- ORF konkret - video on AI for intelligent energy systems
- Thoughts on AI for intelligent energy systems DiePresse, KleineZeitung, Grazer
- Our living lab Innovation District Inffeld
- Critical thoughts on Smart Buildings
Gerald Schweiger - Lab head
Thomas Hirsch - Postdoc
Adil Mukhtar - Postdoc
Johannes Exenberger - PhD student
Gerda Langer - Master student
Cristina Vera Zambrano - Master student
Gillsu George Thekkekara Puthenparampil - Master student
Bernhard Lugger - Master student
Fabian Zehetmair - Student assistant
Richard Kraus - Student assistant
Smart2B: AI-based services to upgrate the smartness of legacy equipment
I-Greta: AI-based solutions to operate highly flexible energy systems benefitting from storage capacities
DomLearn: Domain-Inofrmed ML techniques for intelligent systems
UserGrids: User-centered smart energy systems
WhichWay: Security and Pricavy apsects of IoT platforms
DigitalEnergyTwin: ML-based services for Industry 4.0
BEYOND: Combining physical and ML modelling techniques
Cool-Quarter-Plus: ML-based cooling services
NextGES: Physics-informed ML for different energy services
ANSERS: User-centered energy services
Annex 81: Data-Driven Smart Buildings
PV4EAG: Geo-AI to detect alternative areas for the construction of PV systems