摘要
通过从经济发展水平、产业结构、人口数量、对外贸易、气温因素五个维度,选取八个指标对上海市电力需求进行预测分析,首先从理论上分析了电力需求和各个影响因素之间的关系,接着对上海市2004~2018年各变量的相关数据分别通过单位根检验和Granger因果检验的定性分析以及灰色关联度的定量分析两个方面来筛选模型的影响因素,最后利用主成分分析对建立的多元回归模型进行修正,并根据修正后的回归模型对历史用电量进行模拟预测,得出该模型具有一定的预测精度。研究表明上海市电力需求受上海市国民生产总值、人口数量、第二三产业比值、对外出口、夏季平均气温五个因素影响显著,且模型精度较高,可为电力部门提供参考。
Through eight indicators from five dimensions,including economic development level,industrial structure,population,foreign trade,and temperature factors,to predict and analyze Shanghai's electricity demand.First,it analyzes the electricity demand and the influencing factors theoretically.Then,the relevant data of each variable in Shanghai from 2004 to 2018 was cleaned and processed,and then the influencing factors of the model were screened through the qualitative analysis of unit root test and Granger causality test,and the quantitative analysis of gray correlation degree.Finally,the principal component analysis is used to revise the established multiple regression model,and the historical electricity consumption is simulated and predicted according to the revised regression model,and it is concluded that the model has certain prediction accuracy.Research shows that Shanghai's electricity demand is significantly affected by five factors,namely,Shanghai's GDP,population,ratio of tertiary and secondary industries,exports,and average summer temperature.The model in this paper is highly accurate,which can provide a reference for the power sector.
作者
张游国
高岩
ZHANG Youguo;GAO Yan(School of Management,University of Shanghai for Science and Technology,Shanghai 200093,China)
出处
《物流科技》
2021年第12期26-29,35,共5页
Logistics Sci-Tech
关键词
GRANGER因果检验
灰色关联分析
多元回归预测
主成分分析
Granger causality test
grey relational analysis
multiple regression prediction
principal component analysis
作者简介
张游国(1995-),男,重庆人,上海理工大学管理学院数量经济学硕士研究生,研究方向:工业经济、电力市场;高岩(1962-),男,黑龙江五常人,上海理工大学管理学院,教授,博士生导师,研究方向:工业经济、系统工程、智能电网。
