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In this paper, we propose an energy management agent that controls HVAC facilities and ESS by using the Policy Gradient Method, which is one of the ...
Sep 12, 2019 ， This paper presents a data-driven approach that leverages reinforcement learning to manage the optimal energy consumption of a smart home
Missing: Facilities | Show results with:Facilities
This research proposes the use of RL for the development of a fridge energy management system capable of minimizing energy consumption and optimizing the use ...
Development of Reinforcement Learning-based Energy Management Agent for HVAC Facilities and ESS. K Kwon, S Hong, JH Heo, H Jung, J Park. The Transactions of ...
A reinforcement-learning-based energy management algorithm is proposed to reduce the operation energy costs of the target smart energy building under unknown ...
Missing: HVAC Facilities
This paper introduces a new strategy for managing energy consumption by employing a constrained deep Q-network (DQN) algorithm to regulate Heating, ...
This paper proposes a novel reinforcement learning (RL) architecture for the efficient scheduling and control of the heating, ventilation and air conditioning ...
Missing: Facilities | Show results with:Facilities
Data-driven building energy management systems (BEMS) based on deep reinforcement learning (DRL) have attracted significant research interest, particularly in ...
Abstract—This paper presents a novel methodology to control. HVAC system and minimize energy cost on the premise of satisfying power system constraints.
charging and discharging scheduling of ESS. In the proposed DQN-based model, the ESS agent learns actions until it maximizes the total cumulative reward ...
