Residential Energy Management with Deep Reinforcement Learning
Date of Original Version
A smart home with battery energy storage can take part in the demand response program. With proper energy management, consumers can purchase more energy at off-peak hours than at on-peak hours, which can reduce the electricity costs and help to balance the electricity demand and supply. However, it is hard to determine an optimal energy management strategy because of the uncertainty of the electricity consumption and the real-time electricity price. In this paper, a deep reinforcement learning based approach has been proposed to solve this residential energy management problem. The proposed approach does not require any knowledge about the uncertainty and can directly learn the optimal energy management strategy based on reinforcement learning. Simulation results demonstrate the effectiveness of the proposed approach.
Proceedings of the International Joint Conference on Neural Networks
Wan, Zhiqiang, Hepeng Li, and Haibo He. "Residential Energy Management with Deep Reinforcement Learning." Proceedings of the International Joint Conference on Neural Networks 2018-July, (2018). doi:10.1109/IJCNN.2018.8489210.