Optimization strategy based on deep reinforcement learning for home energy management

Yuankun Liu; Dongxia Zhang; Hoay Beng Gooi

doi:10.17775/CSEEJPES.2019.02890

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Optimization strategy based on deep reinforcement learning for home energy management

Regular Papers | 更新时间：2026-10-15

- Optimization strategy based on deep reinforcement learning for home energy management
- CSEE Journal of Power and Energy Systems Vol. 6, Issue 3, Pages: 572-582(2020)
- 作者机构：
  
  1. China Electric Power Research Institute,Beijing,China
  2. Nanyang Technological University,Singapore,Singapore
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2019.02890
  CLC：
- Published：01 September 2020
- 稿件说明：
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Yuankun Liu, Dongxia Zhang, Hoay Beng Gooi. Optimization strategy based on deep reinforcement learning for home energy management[J]. CSEE Journal of Power and Energy Systems, 2020, 6(3): 572-582.
DOI：

Yuankun Liu, Dongxia Zhang, Hoay Beng Gooi. Optimization strategy based on deep reinforcement learning for home energy management[J]. CSEE Journal of Power and Energy Systems, 2020, 6(3): 572-582. DOI： 10.17775/CSEEJPES.2019.02890.

摘要

Abstract

With the development of a smart grid and smart home

massive amounts of data can be made available

providing the basis for algorithm training in artificial intelligence applications. These continuous improving conditions are expected to enable the home energy management system (HEMS) to cope with the increasing complexities and uncertainties in the enduser side of the power grid system. In this paper

a home energy management optimization strategy is proposed based on deep Q-learning (DQN) and double deep Q-learning (DDQN) to perform scheduling of home energy appliances. The applied algorithms are model-free and can help the customers reduce electricity consumption by taking a series of actions in response to a dynamic environment. In the test

the DDQN is more appropriate for minimizing the cost in a HEMS compared to DQN. In the process of method implementation

the generalization and reward setting of the algorithms are discussed and analyzed in detail. The results of this method are compared with those of Particle Swarm Optimization (PSO) to validate the performance of the proposed algorithm. The effectiveness of applied data-driven methods is validated by using a real-world database combined with the household energy storage model.

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