Long-Term Electricity Load Forecasting Using Artificial Neural Network: The Case Study of Benin.

AuteurCHETANGNY, PATRICE KOFFI
AuteurYOTTO, HABIB CONRAD SOTIMAN
AuteurZOGBOCHI, VICTOR
AuteurAREDJODOUN, JACQUES G
AuteurHOUNDEDAKO, SOSSOU
AuteurBARBIER, GERALD
AuteurVIANOU, ANTOINE
AuteurCHAMAGNE, DIDIER
Date d'ajout2026-06-02T16:06:57Z
Date de disponibilite2026-06-02T16:06:57Z
Date de publication2023
ResumeAfricans in general and specially Beninese’s low rate access to electricity requires efforts to set up new electricity production units. To satistfy the needs, it is therefore very important to have a prior knowledge of the electrical load. In this context, knowing the right need for the electrical energy to be extracted from the Beninese network in the long term and in order to better plan its stability and reliability, a forecast of this electrical load is then necessary. The study has used the annual power grid peak demand data from 2001 to 2020 to develop, train and validate the models. The electrical load peaks until 2030 are estimated as the output value. This article evaluates three algorithms of a method used in artificial neural networks (ANN) to predict electricity consumption, which is the Multilayer Perceptron (MLP) with backpropagation. To ensure stable and accurate predictions, an evaluation approach using mean square error (MSE) and correlation coefficient (R) has been used. The results have proved that the data predicted by the Bayesian regulation variant of the Multilayer Perceptron (MLP), is very close to the real data during the training and the learning of these algorithms. The validated model has developed high generalization capabilities with insignificant prediction deviations.
DOI10.4028/p-zq4id8
Autre identifiantBECDB-14275
URIhttps://dspace.uac.bj/handle/123456789/12177
Languefr
Fait partie deAdvanced Engineering Forum (AEF)
SujetArtificial Neural Network
SujetBayesian Regularization
SujetForecasting Peak Electricity
SujetMulti-Layer Perceptron
TitreLong-Term Electricity Load Forecasting Using Artificial Neural Network: The Case Study of Benin.
TypeArticle

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