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 relatively new and emerging field,… read more
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Call for Papers: CISBAT 2019
CISBAT 2019 – International Scientific Conference 4-6 September 2019, EPFL Lausanne, Switzerland Climate Resilient Cities – Energy Efficiency & Renewables in the Digital Era Focused on energy efficiency and the use of renewables in the built environment, CISBAT offers a dynamic international platform for scientific exchange in fields ranging from… 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 such as electric vehicles, HVAC systems, smart appliances, or batteries. The… read more