Coordinative energy efficiency improvement of buildings based on deep reinforcement learning

被引：2

作者：

Xu C. ^{[1
,2
]}

Li W. ^{[1
,2
]}

Rao Y. ^{[1
,2
]}

Qi B. ^{[1
,2
]}

Yang B. ^{[3
]}

Wang Z. ^{[3
]}

机构：

[1] Research Department, Nari Group Corporation/State Grid Electric Power Research Institute, Nanjing

[2] Research Department, State Grid Electric Power Research Institute Wuhan Efficiency Evaluation Company Limited, Wuhan

[3] Marketing Service Center, State Grid Jiangsu Electric Power Co., Ltd, Nanjing

来源：

Cyber-Physical Systems | 2023年 / 9卷 / 03期

关键词：

Building energy management; deep reinforcement learning; energy efficient; energy storage system; renewable sources;

D O I：

10.1080/23335777.2022.2066181

中图分类号：

学科分类号：

摘要：

Due to the uncertainty of user’s behaviour and other conditions, the design of energy efficiency improvement methods in buildings is challenging. In this paper, a building energy management method based on deep reinforcement learning is proposed, which solves the energy scheduling problem of buildings with renewable sources and energy storage system and minimises electricity costs while maintaining the user’s comfort. Different from model-based methods, the proposed DRL agent makes decisions only by observing the measurable information without considering the dynamic of the building environment. Simulations based on real data verify the effectiveness of the proposed method. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

引用

页码：260 / 272

页数：12

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