基于人体舒适度指数的居民用电分析及用电负荷预测研究  

Research on resident electricity consumption based on human comfort index and power load forecasting

作　　者：卜飞飞 白宏坤 王圆圆 韩丁 BU Feifei;BAI Hongkun;WANG Yuanyuan;HAN Ding(State Grid Henan Economic Research Institute,Zhengzhou 450000,China)

机构地区：[1]国网河南省电力公司经济技术研究院,河南郑州450000

出　　处：《中国测试》2023年第4期85-91,共7页China Measurement & Test

基　　金：国家自然科学基金(51807149)。

摘　　要：针对气象状况、季节等因素对居民用电负荷有不同影响,为深入分析不同因素与居民用电的相关性,提出人体气象舒适度指数,建立适用于用电负荷的人体气象舒适度指数模型。采用灰色关联度方法分析各气象因子以及人体舒适度指数与居民用电的相关性。基于郑州气象大数据和居民用户用电数据,得到人体气象舒适度指数相比于其他单个气象因子具有更强的关联性。基于灰度预测模型和RBF神经网络,结合人体舒适度指数对用电的影响,提出灰色RBF神经网络预测算法。用郑州某小区近年的负荷数据作为预测样本数据,分别采用灰色预测模型、RBF神经网络预测模型以及灰色RBF神经网络预测模型对用户负荷进行预测分析。测试结果表明:灰色RBF神经网络模型预测精度最高,可为后续居民用电负荷的精确预测奠定理论基础。In view of the fact that meteorological conditions,seasons and other influencing factors have different effects on residential electricity load,in order to deeply analyze the correlation between different factors and residential electricity consumption,the human meteorological comfort index is proposed.And a human meteorological comfort index model suitable for electricity load is established.The grey correlation degree method is used to analyze the correlation between meteorological factors,human comfort index and residential electricity consumption.Based on the electricity consumption data of Zhengzhou meteorological big data and residential users,it is found that the human meteorological comfort index has a stronger correlation with other single meteorological factors.Based on RBF neural network,making full use of human comfort index and grey prediction,a grey RBF neural network prediction algorithm is proposed.The load data of a residential district in Zhengzhou in recent years are used as the forecasting sample data,and the grey forecasting model,RBF neural network forecasting model and grey RBF neural network forecasting model are used to forecast and analyze the user load.The test results show that the grey RBF neural network model has the highest prediction accuracy,which lays a theoretical foundation for the accurate prediction of residential power load.

关 键 词：人体气象舒适度指数 气象因子 灰色关联度 灰色RBF神经网络 

分 类 号：TH133[机械工程—机械制造及自动化] TB9[一般工业技术—计量学]