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。
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.
作者
王枭
刘清
Alaa SHAKIR
张靖邦
王驰
WANG Xiao;LIU Qing;Alaa SHAKIR;ZHANG Jingbang;WANG Chi(School of Automation,Wuhan University of Technology,Wuhan 430070,China;School of Electrical Engineering and Automation,Wuhan University,Wuhan 430072,China;State Grid Liaoning Electric Power Co.,Ltd.,Shenyang 110004,China)
出处
《电力系统自动化》
EI
CSCD
北大核心
2024年第15期84-91,共8页
Automation of Electric Power Systems
基金
国家自然科学基金资助项目(52207134)
湖北省自然科学基金资助项目(2022CFB678)
国家水运安全工程技术研究中心开放基金资助项目(A202401)。
关键词
智能楼宇
暖通空调
能量管理
模型预测控制
修正线性单元
数据驱动控制
intelligent building
heating,ventilation,and air conditioning(HVAC)
energy management
model predictive control
