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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 ...
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
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 ...
This paper introduces a new strategy for managing energy consumption by employing a constrained deep Q-network (DQN) algorithm to regulate Heating, ...
People also ask
What is energy management in HVAC systems?
Identify energy waste patterns, set optimal temperatures and systems operational hours to maximize energy savings and cut costs.
What is reinforcement learning agent?
Agent: In Reinforcement Learning, an entity that interacts with the environment to learn optimal behavior. Environment: The context or state within which the agent operates. Action: A decision made by the agent that can alter the state of the environment in reinforcement learning.
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 ...
Jul 23, 2024 ， This research addresses the pressing need for enhanced energy management in smart homes, motivated by the inefficiencies of current methods.
May 4, 2022 ， The recent development of deep learning has enabled deep reinforcement learning (DRL) to drive optimal policies for sophisticated and capable.
charging and discharging scheduling of ESS. In the proposed DQN-based model, the ESS agent learns actions until it maximizes the total cumulative reward ...
