We develop reinforcement learning techniques for energy efficient operation of buildings and systems without the need for mathematical models. Despite the many advantages of RL for application in the built environment, many challenges remain, and are explored in our research. (1) As RL is a Read more
Reinforcement learning for urban energy systems & demand response
Demand response, or demand-side management, improves grid stability by increasing demand flexibility, and shifts peak demand towards periods of peak renewable energy generation by providing consumers with economic incentives. Reinforcement learning has been utilized to control diverse energy systems Read more
Multi-Agent Reinforcement Learning for demand response & building coordination
We have introduced a new simulation environment that is the result of merging CitySim, a building energy simulator, and TensorFlow, a powerful machine learning library for deep learning. This new simulation environment has the potential for developing building energy scenarios in which machine Read more
IEA-EBC Annex 79: Occupant Centric Design and Operation of Buildings
A new IEA EBC Annex on occupant-centric design and controls has been approved. The Annex will involve about 100 experts from 20 countries for five years and focus on fundamental research issues regarding occupant comfort and behaviour, application to design and operations practice, new technology Read more
Occupant centered building control
The energy savings of buildings promised by automatic control systems are often ineffective due to occupants who may not use the features of the system to the full potential at best, or deactivate the system at worst. Therefore, systems that adapt to the occupant over time without compromising Read more
