文献类型：期刊文献

英文题名：Meta-Reinforcement Learning-Based Transferable Scheduling Strategy for Energy Management

作者：Xiong, Luolin[1,2];Tang, Yang[1,2];Liu, Chensheng[1,2];Mao, Shuai[3];Meng, Ke[4];Dong, Zhaoyang[5];Qian, Feng[1,2]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Engn Res Ctr Proc Syst Engn, Minist Educ, Shanghai 200237, Peoples R China;[3]Nantong Univ, Dept Elect Engn, Nantong 226019, Peoples R China;[4]Univ New South Wales, Sch Elect Engn & Telecommun, Sydney, NSW 2052, Australia;[5]Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore

年份：2023

卷号：70

期号：4

起止页码：1685

外文期刊名：IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS

收录：;EI(收录号：20231413848705);WOS:【SCI-EXPANDED(收录号:WOS:001011368200022)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 61988101, Grant 62293502, and Grant 62293504; in part by the Program of Shanghai Academic Research Leader under Grant 20XD1401300; and in part by the Fundamental Research Funds for the Central Universities. This article was recommended by Associate Editor P. Shi. (Corresponding authors: Yang Tang; Feng Qian.)

语种：英文

外文关键词：Home energy management system; transferable scheduling strategies; meta-reinforcement learning; long shortterm memory

摘要：In Home Energy Management System (HEMS), the scheduling of energy storage equipment and shiftable loads has been widely studied to reduce home energy costs. However, existing data-driven methods can hardly ensure the transferability amongst different tasks, such as customers with diverse preferences, appliances, and fluctuations of renewable energy in different seasons. This paper designs a transferable scheduling strategy for HEMS with different tasks utilizing a Meta-Reinforcement Learning (Meta-RL) framework, which can alleviate data dependence and massive training time for other data-driven methods. Specifically, a more practical and complete demand response scenario of HEMS is considered in the proposed Meta-RL framework, where customers with distinct electricity preferences, as well as fluctuating renewable energy in different seasons are taken into consideration. An inner level and an outer level are integrated in the proposed Meta-RL-based transferable scheduling strategy, where the inner and the outer level ensure the learning speed and appropriate initial model parameters, respectively. Moreover, Long Short-Term Memory (LSTM) is presented to extract the features from historical actions and rewards, which can overcome the challenges brought by the uncertainties of renewable energy and the customers' loads, and enhance the robustness of scheduling strategies. A set of experiments conducted on practical data of Australia's electricity network verify the performance of the transferable scheduling strategy.