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This paper presents a deep reinforcement learning (DRL) approach for controlling distributed energy resources (DERs) such as HVAC and battery storage in buildings, aiming to enhance energy efficiency and load flexibility. The proposed DRL algorithm, which uses a deep deterministic policy gradient method, was tested in a physical building and demonstrated cost savings of up to 39.6% compared to traditional rule-based control strategies while maintaining thermal comfort. The authors also made the simulation c

Author

xinyuusc

Language

EN