Optimization of Short-Term Forecast of Electric Power Demand in the city of Yaoundé-Cameroon by a hybrid model based on the combination of neural networks and econometric methods from a designed energy optimization algorithm

Electrical load forecasting has become a very important task for the management and planning of electrical energy. Several methods have been developed in the literature to solve this task, but researchers continue to search for a stable model that minimizes the prediction error as much as possible....

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Published inTechnological forecasting & social change Vol. 187; p. 122212
Main Authors Dieudonné, Nzoko Tayo, Armel, Talla Konchou Franck, Hermann, Djeudjo Temene, Vidal, Aloyem Kaze Claude, René, Tchinda
Format Journal Article
LanguageEnglish
Published Elsevier Inc 01.02.2023
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ISSN0040-1625
1873-5509
DOI10.1016/j.techfore.2022.122212

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Abstract Electrical load forecasting has become a very important task for the management and planning of electrical energy. Several methods have been developed in the literature to solve this task, but researchers continue to search for a stable model that minimizes the prediction error as much as possible. In this article, we propose a hybrid model of artificial intelligence and statistical methods, designed from an optimization algorithm for hourly forecasts of electricity over a period of one week. To do this, we started with a comparative study of predictions with models of artificial neural networks (ANN), multiple linear regression (LRM) and Holt exponential smoothing (HES). Then we retained the best parameters of each model to design our hybrid model (ANN-LRM-HES). The results obtained by each model are considered acceptable in view of the statistical indicators. These results show that apart from Deep Learning techniques which offer excellent results the hybrid model (ANN-LRM-HES) provides the best prediction results, followed by the ANN, LRM and HES models respectively. From the comparative study it emerges that the proposed hybrid model outperformed most similar models in the literature on the subject, obtaining statistically significant precision values. •The hybrid model (ANN-LRM-HES) provides one of the best prediction results.•Forecasting is important to improve the end use of energy.•Cameroon is an emergent country.
AbstractList Electrical load forecasting has become a very important task for the management and planning of electrical energy. Several methods have been developed in the literature to solve this task, but researchers continue to search for a stable model that minimizes the prediction error as much as possible. In this article, we propose a hybrid model of artificial intelligence and statistical methods, designed from an optimization algorithm for hourly forecasts of electricity over a period of one week. To do this, we started with a comparative study of predictions with models of artificial neural networks (ANN), multiple linear regression (LRM) and Holt exponential smoothing (HES). Then we retained the best parameters of each model to design our hybrid model (ANN-LRM-HES). The results obtained by each model are considered acceptable in view of the statistical indicators. These results show that apart from Deep Learning techniques which offer excellent results the hybrid model (ANN-LRM-HES) provides the best prediction results, followed by the ANN, LRM and HES models respectively. From the comparative study it emerges that the proposed hybrid model outperformed most similar models in the literature on the subject, obtaining statistically significant precision values. •The hybrid model (ANN-LRM-HES) provides one of the best prediction results.•Forecasting is important to improve the end use of energy.•Cameroon is an emergent country.
ArticleNumber 122212
Author René, Tchinda
Armel, Talla Konchou Franck
Vidal, Aloyem Kaze Claude
Dieudonné, Nzoko Tayo
Hermann, Djeudjo Temene
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Keywords Exponential smoothing
Deep neural network
Hybrid model (ANN-LRM-HES)
Multiple linear regression
Electricity
Short-term load prediction
Language English
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Snippet Electrical load forecasting has become a very important task for the management and planning of electrical energy. Several methods have been developed in the...
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StartPage 122212
SubjectTerms Deep neural network
Electricity
Exponential smoothing
Hybrid model (ANN-LRM-HES)
Multiple linear regression
Short-term load prediction
Title Optimization of Short-Term Forecast of Electric Power Demand in the city of Yaoundé-Cameroon by a hybrid model based on the combination of neural networks and econometric methods from a designed energy optimization algorithm
URI https://dx.doi.org/10.1016/j.techfore.2022.122212
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