Model and Data Hybrid Driven Approach for Quantifying the Meteorology-Dependent Demand Flexibility of Building Thermal Loads  

作　　者：Bo Hu Xin Cheng Changzheng Shao Tao Niu Chunyan Li Yue Sun Wei Huang Kaigui Xie 

机构地区：[1]State Key Laboratory of Power Transmission Equipment and System Security,Chongqing University,Chongqing,400030,China

出　　处：《CSEE Journal of Power and Energy Systems》2025年第1期394-405,共12页中国电机工程学会电力与能源系统学报(英文)

基　　金：supported by the National Natural Science Foundation of China(52107072);International Cooperation and Exchange of NSFC(51861145406).

摘　　要：Recently, the heat and electricity integrated energy system (HE-IES) has become a hot topic in both industry and academia. In the HE- IES, the potential flexibility of the buildings' thermal loads can be exploited to relax the heat power balance constraints and consequently allow a more flexible operation of the combined heat and power units. In this paper, model-driven and data-driven techniques are combined to quantify the demand flexibility of the buildings' thermal loads in a non-instructive way. First, the explicit analytical equivalent thermal parameter (ETP) model of the aggregated buildings is developed. The heat transfer coefficient (k) and thermal inertia coefficient (C) of the ETP model are designated to measure the potential demand flexibility. Second, the Particle Swarm Optimization optimized Radial Basis Function neural network (PSO-RBF) is used to identify the relationship between the values of k and C and the meteorological factors. To obtain the training data, an innovative two-stage regression method based on the adaptive temporal resolution is proposed to extract k and C values from the historical thermal load data. Finally, a flexible thermal load model is built based on the predictions of the meteorological factors, which can be conveniently incorporated into the online dispatch of the HE-IES. A comprehensive simulation environment is designed to verify the accuracy and availability of the proposed technique.

关 键 词：Flexibility of buildings'thermal loads heat and electricity integrated energy system meteorological factors model and data hybrid driven 

分 类 号：TM73[电气工程—电力系统及自动化]