A deep reinforcement learning based approach for home energy management system
Date of Original Version
Home energy management system (HEMS) enables residents to actively participate in demand response (DR) programs. It can autonomously optimize the electricity usage of home appliances to reduce the electricity cost based on time-varying electricity prices. However, due to the existence of randomness in the pricing process of the utility and resident's activities, developing an efficient HEMS is challenging. To address this issue, we propose a novel home energy management method for optimal scheduling of different kinds of home appliances based on deep reinforcement learning (DRL). Specifically, we formulate the home energy management problem as an MDP considering the randomness of real-time electricity prices and resident's activities. A DRL approach based on proximal policy optimization (PPO) is developed to determine the optimal DR scheduling strategy. The proposed approach does not need any information on the appliances' models and distribution knowledge of the randomness. Simulation results verify the effectiveness of our proposed approach.
Publication Title, e.g., Journal
2020 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2020
Li, Hepeng, Zhiqiang Wan, and Haibo He. "A deep reinforcement learning based approach for home energy management system." 2020 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2020 (2020). doi: 10.1109/ISGT45199.2020.9087647.