不同舒适度指数在最大电力负荷预测中的应用

Application of different comfort indexes in maximum electric power load forecasting

利用2008—2019年湖北省荆州、荆门、宜昌、咸宁、随州地区日最大电力负荷值和同期国家气象观测站气象资料,分析最大气象负荷的变化率(L_(pm))与温湿指数(I)、气象敏感负荷条件指数(MSLI)、人体舒适度(ET)、体感温度指数(Te)等4种舒适度指数和温度的关系,采用多元回归和BP神经网络方法建立基于上述4种舒适度指数的日最大电力负荷预测模型。结果表明:L_(pm)与气温、4种舒适度指数在夏季呈正相关,冬季呈负相关,且夏季相关性比冬季显著;综合温度、湿度、风速的4种舒适度指数的变化能够引起L_(pm)的变化,且这种变化在夏季,尤其是7月和8月更明显;BP神经网络模型和多元回归模型的误差基本控制在电力部门的要求范围内;BP神经网络预测效果优于多元回归,后期业务应用中荆门和咸宁地区建议选取ET指数,其他地市4种指数皆可。

Based on the daily maximum power load values in Jingzhou,Jingmen,Yichang,Xianning and Suizhou areas of Hubei Province from 2008 to 2019 and the meteorological data of national meteorological observation stations in the same period,the relationships between the change rate of maximum meteorological load(L_(pm)),four comfort indexes such as temperature and humidity index(I),meteorological sensitive load index(MSLI),human comfort(ET)and somatosensory temperature index(Te)and temperature were analyzed.The daily maximum power load forecasting models were established based on the above four comfort indexes by using multiple regression and BP neural network method.The results show that the L_(pm) was positively correlated with temperature and the above four comfort indexes in summer,negatively correlated in winter,and the correlation was significantly higher in summer than in winter.The changes of the above four comfort indexes integrating temperature,humidity and wind speed could cause the change of the L_(pm),and this change was more obvious in summer,especially in July and August.The errors of BP neural network model and multiple regression model were basically controlled within the requirements of the power department.The prediction effect of BP neural network was better than that of multiple regression.In the later business application,it was suggested to select ET index in Jingmen and Xianning,and four indexes in other cities can be used.