Energy Management Strategy for Heating, Ventilation, and Air Conditioning Systems in Buildings Based on Multi-layer Rectified Linear Unit Network

WANG Xiao; LIU Qing; Alaa SHAKIR; ZHANG Jingbang; WANG Chi

doi:10.7500/AEPS20231120001

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Energy Management Strategy for Heating, Ventilation, and Air Conditioning Systems in Buildings Based on Multi-layer Rectified Linear Unit Network

Basic Research | 更新时间：2025-06-16

- Energy Management Strategy for Heating, Ventilation, and Air Conditioning Systems in Buildings Based on Multi-layer Rectified Linear Unit Network
- Automation of Electric Power Systems Vol. 48, Issue 15, Pages: 84-91(2024)
- 作者机构：
  
  1.武汉理工大学自动化学院,湖北省武汉市 430070
  2.武汉大学电气与自动化学院,湖北省武汉市 430072
  3.国网辽宁省电力有限公司,辽宁省沈阳市 110004
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(52207134);Hubei Provincial Natural Science Fundation of China(2022CFB678);National Engineering Research Center for Water Transport Safety(A202401)
- DOI：10.7500/AEPS20231120001
  CLC：
- Received：20 November 2023，
  
  Revised：2024-01-19，
  
  Published Online：04 June 2024，
  
  Published：10 August 2024
- 稿件说明：
移动端阅览
王枭,刘清,Alaa SHAKIR等.基于多层ReLU网络的楼宇暖通空调系统能量管理策略[J].电力系统自动化,2024,48(15):84-91.

WANG Xiao,LIU Qing,SHAKIR Alaa,et al.Energy Management Strategy for Heating, Ventilation, and Air Conditioning Systems in Buildings Based on Multi-layer Rectified Linear Unit Network[J].Automation of Electric Power Systems,2024,48(15):84-91.
王枭,刘清,Alaa SHAKIR等.基于多层ReLU网络的楼宇暖通空调系统能量管理策略[J].电力系统自动化,2024,48(15):84-91. DOI： 10.7500/AEPS20231120001.

WANG Xiao,LIU Qing,SHAKIR Alaa,et al.Energy Management Strategy for Heating, Ventilation, and Air Conditioning Systems in Buildings Based on Multi-layer Rectified Linear Unit Network[J].Automation of Electric Power Systems,2024,48(15):84-91. DOI： 10.7500/AEPS20231120001.

摘要

提升现代智能楼宇的能量管理能力，是电力紧平衡背景下促进电网节能增效的重要举措。文中提出一种数据驱动的楼宇暖通空调系统节能控制策略，避免了传统模型预测控制对精确热力学建模的依赖。首先

在搭建楼宇热网络仿真模型的基础上，采用延时嵌入法构造输入特征，建立基于修正线性单元的多层神经网络模型，实现室内温度时间序列的多步预测。然后，针对电网的分时电价，构建滚动时域优化模型，并将其重构为混合整数线性规划的形式，实现有限控制周期内优化模型的高效求解。最后，基于Simscape搭建楼宇热仿真模型，验证了所提控制策略的有效性。仿真结果表明，所提策略能够满足温度舒适度要求，并提高楼宇的经济运行水平。

Abstract

Improving the energy management capability of modern intelligent buildings is an important measure to promote energy saving and efficiency enhancement of power grid under the background of tight power balance. This paper proposes a data-driven energy saving control strategy for heating

ventilation

and air conditioning (HVAC) systems in buildings

which avoids the dependence of traditional model predictive control on accurate thermodynamic modeling. First

on the basis of thermal network simulation model for buildings

the input features are constructed by time-delay embedding method

and a multi-layer neural network model based on rectified linear unit (ReLU) is established to realize the multi-step prediction of indoor temperature time series. Then

for the time-of-use price of power grid

the rolling time-domain optimization model is constructed and reconstructed into the form of mixed-integer linear programming to realize the efficient solution of the optimization model during the finite control period. Finally

a thermal simulation model for buildings is built based on Simscape to verify the effectiveness of the proposed control strategy. The simulation results show that the proposed strategy can meet the requirements of temperature comfort and improve the economic operation level of the building.

关键词

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references

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