Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/148706
Title: Optimization strategy based on deep reinforcement learning for home energy management
Authors: Liu, Yuankun
Zhang, Dongxia
Gooi, Hoay Beng
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2020
Source: Liu, Y., Zhang, D. & Gooi, H. B. (2020). Optimization strategy based on deep reinforcement learning for home energy management. CSEE Journal of Power and Energy Systems, 6(3), 572-582. https://dx.doi.org/10.17775/CSEEJPES.2019.02890
Journal: CSEE Journal of Power and Energy Systems
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.
URI: https://hdl.handle.net/10356/148706
ISSN: 2096-0042
DOI: 10.17775/CSEEJPES.2019.02890
Rights: © 2019 The Author(s) (published by CSEE). This is an open-access article distributed under the terms of the Creative Commons Attribution License.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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