基于粒子群优化算法的空调负荷灰箱模型辨识  

Identification of grey box model for air conditioning load based on particle swarm optimization algorithm

作　　者：朱明 夏宇栋 常凯 王志梁 ZHU Ming;XIA Yudong;CHANG Kai;WANG Zhiliang(School of Automation,Hangzhou Dianzi University,Hangzhou 310018,China)

机构地区：[1]杭州电子科技大学自动化学院,浙江杭州310018

出　　处：《电力科学与技术学报》2023年第4期214-221,共8页Journal of Electric Power Science And Technology

基　　金：浙江省自然科学基金(LQ19E060007);浙江省重点研发计划(2020C01164)。

摘　　要：较高精度的空调负荷模型是开发实施有效空调控制策略的重要依据,其有利于促进减小电力能源消耗以节约用电成本。首先,通过对建筑构造、室内外环境和气象因素等影响分析,搭建可用于预测空调负荷的灰箱模型,即三阶的等效热参数模型以及二阶的等效湿阻模型;接着,通过最小化模型输出室内温湿度数据与室内实测温湿度采样数据之间的误差建立优化目标函数;然后,提出并使用基于粒子群优化算法的参数辨识方法获取灰箱模型关键参数。实验研究表明,辨识得到的等效热阻和湿阻模型能准确地反映室内温湿度分布和变化特性,具有预测空调负荷的实际应用价值。A higher-precision air conditioning load model serves as a crucial foundation for developing and implementing effective air conditioning control strategies,which is conducive to reducing electricity consumption and saving power costs.Firstly,by analyzing the impact of building structure,indoor and outdoor environment,and meteorological factors,a grey-box model is constructed for predicting air conditioning loads..This model consists of a third-order equivalent thermal parameter model and a second-order equivalent moisture resistance model.Subsequently,the optimization objective function is established by minimizing the error between the indoor temperature and humidity output from the model and the measured temperature and humidity.Then,a parameter identification method based on the particle swarm optimization(PSO)algorithm is proposed and employed to obtain the crucial parameters of the grey-box model.Experimental studies demonstrate that the identified equivalent thermal resistance and moisture resistance models accurately reflect the indoor temperature and humidity distribution and variation characteristics,thus possessing practical application value in predicting air conditioning loads.

关 键 词：灰箱模型 空调负荷建模 系统参数辨识 粒子群优化算法 

分 类 号：TM9[电气工程—电力电子与电力传动]