摘要
气象因素是电力需求变化的重要影响因素之一.为了研究气候变化对月度电力需求的影响,建立了包含气象条件、时间趋势和春节移动假日等变量的多元回归方程,以苏州市月度电力需求预测为例,重点分析了气象因素和春节移动假日对月度电力需求的影响.研究结果发现,使用月平均气温计算出的月制冷度(Cooling Degree of Month,CDM)和月采暖度(Heating Degree of Month,HDM)能较好的刻画气温变化对月度电力需求的影响;考虑了春节假日效应后,能更好的提升回归方程的预测能力;非"疫情"期间和"疫情"期间模拟预测结果均达到较好的精度,显示出模型具有良好的预测稳健性.
It is common knowledge that meteorological factors influence the variation of monthly electricity demand.In this paper,a multiple regression model was developed to forecast monthly electricity demand in Suzhou,China.The results reveal that CDM(cooling degree of month)and HDM(heating degree of month)variables which are computed based on monthly average temperature can capture the fuctuation of monthly electricity demand effectively,and due to the consideration of Spring Festival moving holidays,the forecast performance of model have been improved.The adopted model also exhibits the robust forecasting characteristics during period of 2020.01-2020.12 which affected by COVID-19.
作者
苏振宇
林军
SU Zhen-yu;LIN Jun(Business School,Gansu University of Political Science and Law,Lanzhou 730070,China)
出处
《数理统计与管理》
北大核心
2023年第2期315-325,共11页
Journal of Applied Statistics and Management
关键词
月度电力需求
预测
温度
气象因素
monthly electricity demand
forecasting
temperature
climatic variables
