文章基本信息

标题：Methodology for forecasting electricity consumption by Grey and Vector autoregressive models
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作者：Serge Guefano ; Jean Gaston Tamba ; Tchitile Emmanuel Wilfried Azong 等
期刊名称：MethodsX
印刷版ISSN：2215-0161
电子版ISSN：2215-0161
出版年度：2021
卷号：8
页码：1-9
DOI：10.1016/j.mex.2021.101296
语种：English
出版社：Elsevier
摘要：Highlights•The Grey and Vector autoregressive models are coupled to improve their accuracy.•Five economic and demographic parameters are included in the new hybrid model.•This new model is a reliable forecasting tool for assessing energy demand.AbstractForecasting energy demand in general, and electricity demand in particular, requires the developing reliable forecasting tools that can be used to monitor the evolution of consumers’ energy needs more accurately. The proposed new hybrid GM(1,1)-VAR(1) model is meant for that purpose. The latter is based on the Grey and Vector autoregressive approaches, and makes it possible to predict future demand, by taking into account economic and demographic determinants with an exponential growth trend. With an associated APE of 1.5, a MAPE of 1.628%, and an RMSE of 15.42, this new model thus presents better accuracy indicators than hybrid models of the same nature. Also, it proves to be as accurate as some recent hybrid artificial intelligence models. The model is thus a reliable forecasting tool that can be used to monitor the evolution of energy demand.•The Grey and Vector autoregressive models are coupled to improve their accuracy.•Five economic and demographic parameters are included in the new hybrid model.•This new model is a reliable forecasting tool for assessing energy demand.Graphical abstractDisplay Omitted
关键词：Forecast;Electricity consumption;Grey model;VAR model;hybrid model GM(1,1)-VAR(1)