Reinforcement Learning for the occupant-centric operation of building energy systems: Theoretical and experimental investigations
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HEPIA Geneva University of Applied Sciences and Arts Western Switzerland2021
Deep Reinforcement Learning (DRL) recently emerged as a possibility to control complex systems without the need to model them. However, since weeks long experiments are needed to assess the performance of a building controller, people still have to rely on ...
Prescribing optimal operation based on the condition of the system, and thereby potentially prolonging its remaining useful lifetime, has tremendous potential in terms of actively managing the availability, maintenance, and costs of complex systems. Reinfo ...
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The built environment currently represents the largest sector in terms of final energy consumption, both in Switzerland and the European Union. Most of the associated energy services, such as space heating and potable hot water preparation, are mainly sati ...
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We introduce a sampling perspective to tackle the challenging task of training robust Reinforcement Learning (RL) agents. Leveraging the powerful Stochastic Gradient Langevin Dynamics, we present a novel, scalable two-player RL algorithm, which is a sampli ...
Buildings account for 35% of the global final energy demand. Efficiency improvements and advanced control strategies have a significant impact in the reduction of energy costs and CO2 emissions. Building energy simulation is widely used to help planners, c ...