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Methodology for forecasting electricity consumption by Grey and Vector autoregressive models

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 b...

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Autores principales: Guefano, Serge, Tamba, Jean Gaston, Azong, Tchitile Emmanuel Wilfried, Monkam, Louis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8374266/
https://www.ncbi.nlm.nih.gov/pubmed/34434816
http://dx.doi.org/10.1016/j.mex.2021.101296
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author Guefano, Serge
Tamba, Jean Gaston
Azong, Tchitile Emmanuel Wilfried
Monkam, Louis
author_facet Guefano, Serge
Tamba, Jean Gaston
Azong, Tchitile Emmanuel Wilfried
Monkam, Louis
author_sort Guefano, Serge
collection PubMed
description 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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spelling pubmed-83742662021-08-24 Methodology for forecasting electricity consumption by Grey and Vector autoregressive models Guefano, Serge Tamba, Jean Gaston Azong, Tchitile Emmanuel Wilfried Monkam, Louis MethodsX Method Article 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. Elsevier 2021-03-04 /pmc/articles/PMC8374266/ /pubmed/34434816 http://dx.doi.org/10.1016/j.mex.2021.101296 Text en © 2021 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Method Article
Guefano, Serge
Tamba, Jean Gaston
Azong, Tchitile Emmanuel Wilfried
Monkam, Louis
Methodology for forecasting electricity consumption by Grey and Vector autoregressive models
title Methodology for forecasting electricity consumption by Grey and Vector autoregressive models
title_full Methodology for forecasting electricity consumption by Grey and Vector autoregressive models
title_fullStr Methodology for forecasting electricity consumption by Grey and Vector autoregressive models
title_full_unstemmed Methodology for forecasting electricity consumption by Grey and Vector autoregressive models
title_short Methodology for forecasting electricity consumption by Grey and Vector autoregressive models
title_sort methodology for forecasting electricity consumption by grey and vector autoregressive models
topic Method Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8374266/
https://www.ncbi.nlm.nih.gov/pubmed/34434816
http://dx.doi.org/10.1016/j.mex.2021.101296
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