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 in | Technological forecasting & social change Vol. 187; p. 122212 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
Elsevier Inc
01.02.2023
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ISSN | 0040-1625 1873-5509 |
DOI | 10.1016/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. |
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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 |
Author_xml | – sequence: 1 givenname: Nzoko Tayo surname: Dieudonné fullname: Dieudonné, Nzoko Tayo organization: Environmental Energy Technologies Laboratory (EETL), Department of Physics, University of Yaounde I, P.O Box 812, Yaounde, Cameroon – sequence: 2 givenname: Talla Konchou Franck surname: Armel fullname: Armel, Talla Konchou Franck email: tkfarmel@yahoo.fr organization: Environmental Energy Technologies Laboratory (EETL), Department of Physics, University of Yaounde I, P.O Box 812, Yaounde, Cameroon – sequence: 3 givenname: Djeudjo Temene surname: Hermann fullname: Hermann, Djeudjo Temene organization: Environmental Energy Technologies Laboratory (EETL), Department of Physics, University of Yaounde I, P.O Box 812, Yaounde, Cameroon – sequence: 4 givenname: Aloyem Kaze Claude surname: Vidal fullname: Vidal, Aloyem Kaze Claude organization: Department of Energetic, Environment and Thermal Engineering, UR-ISIE, University Institute of Technology Fotso Victor, University of Dschang, P.O Box 134, Bandjoun, Cameroon – sequence: 5 givenname: Tchinda surname: René fullname: René, Tchinda organization: Department of Energetic, Environment and Thermal Engineering, UR-ISIE, University Institute of Technology Fotso Victor, University of Dschang, P.O Box 134, Bandjoun, Cameroon |
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Cites_doi | 10.1016/j.renene.2021.02.017 10.1016/j.rser.2015.08.035 10.1007/s40032-018-0464-4 10.3390/en4030488 10.1109/MSP.2012.2186531 10.1016/j.rser.2017.02.085 10.1002/er.5523 10.1111/j.2517-6161.1975.tb01532.x 10.1109/59.76685 10.1016/j.ijforecast.2008.08.003 10.1016/S0378-7796(96)01077-2 10.1016/j.rser.2013.08.055 |
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Keywords | Exponential smoothing Deep neural network Hybrid model (ANN-LRM-HES) Multiple linear regression Electricity Short-term load prediction |
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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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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 |
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