Guefano S, Tamba JG, Azong TEW, Monkam L. Methodology for forecasting electricity consumption by Grey and Vector autoregressive models.
MethodsX 2021;
8:101296. [PMID:
34434816 PMCID:
PMC8374266 DOI:
10.1016/j.mex.2021.101296]
[Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 02/28/2021] [Indexed: 11/19/2022] Open
Abstract
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.
Forecasting 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.
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