Short-Term Electricity Generation Forecasting Using Machine Learning Algorithms: A Case Study of the Benin Electricity Community (C.E.B)
Time series forecasting in the energy sector is important to power utilities for decision making to ensure the sustainability and quality of electricity supply, and the stability of the power grid. Unfortunately, the presence of certain exogenous factors such as weather conditions, electricity pric...
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| Format: | Article |
| Language: | English |
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TIB Open Publishing
2021-06-01
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| Series: | TH Wildau Engineering and Natural Sciences Proceedings |
| Subjects: | |
| Online Access: | https://www.tib-op.org/ojs/index.php/th-wildau-ensp/article/view/25 |
| _version_ | 1818471559945256960 |
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| author | Agbassou Guenoupkati Adekunlé Akim Salami Mawugno Koffi Kodjo Kossi Napo |
| author_facet | Agbassou Guenoupkati Adekunlé Akim Salami Mawugno Koffi Kodjo Kossi Napo |
| author_sort | Agbassou Guenoupkati |
| collection | DOAJ |
| description |
Time series forecasting in the energy sector is important to power utilities for decision making to ensure the sustainability and quality of electricity supply, and the stability of the power grid. Unfortunately, the presence of certain exogenous factors such as weather conditions, electricity price complicate the task using linear regression models that are becoming unsuitable. The search for a robust predictor would be an invaluable asset for electricity companies. To overcome this difficulty, Artificial Intelligence differs from these prediction methods through the Machine Learning algorithms which have been performing over the last decades in predicting time series on several levels. This work proposes the deployment of three univariate Machine Learning models: Support Vector Regression, Multi-Layer Perceptron, and the Long Short-Term Memory Recurrent Neural Network to predict the electricity production of Benin Electricity Community. In order to validate the performance of these different methods, against the Autoregressive Integrated Mobile Average and Multiple Regression model, performance metrics were used. Overall, the results show that the Machine Learning models outperform the linear regression methods. Consequently, Machine Learning methods offer a perspective for short-term electric power generation forecasting of Benin Electricity Community sources.
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| first_indexed | 2024-04-14T03:52:53Z |
| format | Article |
| id | doaj.art-c5b6de57ad864e0c86f03d2e10d3d0d4 |
| institution | Directory Open Access Journal |
| issn | 2748-8829 |
| language | English |
| last_indexed | 2024-04-14T03:52:53Z |
| publishDate | 2021-06-01 |
| publisher | TIB Open Publishing |
| record_format | Article |
| series | TH Wildau Engineering and Natural Sciences Proceedings |
| spelling | doaj.art-c5b6de57ad864e0c86f03d2e10d3d0d42022-12-22T02:13:57ZengTIB Open PublishingTH Wildau Engineering and Natural Sciences Proceedings2748-88292021-06-01110.52825/thwildauensp.v1i.25Short-Term Electricity Generation Forecasting Using Machine Learning Algorithms: A Case Study of the Benin Electricity Community (C.E.B)Agbassou Guenoupkati0Adekunlé Akim Salami1Mawugno Koffi Kodjo2Kossi Napo3University of LoméUniversity of LoméUniversity of LoméUniversity of Lomé Time series forecasting in the energy sector is important to power utilities for decision making to ensure the sustainability and quality of electricity supply, and the stability of the power grid. Unfortunately, the presence of certain exogenous factors such as weather conditions, electricity price complicate the task using linear regression models that are becoming unsuitable. The search for a robust predictor would be an invaluable asset for electricity companies. To overcome this difficulty, Artificial Intelligence differs from these prediction methods through the Machine Learning algorithms which have been performing over the last decades in predicting time series on several levels. This work proposes the deployment of three univariate Machine Learning models: Support Vector Regression, Multi-Layer Perceptron, and the Long Short-Term Memory Recurrent Neural Network to predict the electricity production of Benin Electricity Community. In order to validate the performance of these different methods, against the Autoregressive Integrated Mobile Average and Multiple Regression model, performance metrics were used. Overall, the results show that the Machine Learning models outperform the linear regression methods. Consequently, Machine Learning methods offer a perspective for short-term electric power generation forecasting of Benin Electricity Community sources. https://www.tib-op.org/ojs/index.php/th-wildau-ensp/article/view/25Linear regression modelsShort-term forecastingElectric power generationMachine Learning Algorithms |
| spellingShingle | Agbassou Guenoupkati Adekunlé Akim Salami Mawugno Koffi Kodjo Kossi Napo Short-Term Electricity Generation Forecasting Using Machine Learning Algorithms: A Case Study of the Benin Electricity Community (C.E.B) TH Wildau Engineering and Natural Sciences Proceedings Linear regression models Short-term forecasting Electric power generation Machine Learning Algorithms |
| title | Short-Term Electricity Generation Forecasting Using Machine Learning Algorithms: A Case Study of the Benin Electricity Community (C.E.B) |
| title_full | Short-Term Electricity Generation Forecasting Using Machine Learning Algorithms: A Case Study of the Benin Electricity Community (C.E.B) |
| title_fullStr | Short-Term Electricity Generation Forecasting Using Machine Learning Algorithms: A Case Study of the Benin Electricity Community (C.E.B) |
| title_full_unstemmed | Short-Term Electricity Generation Forecasting Using Machine Learning Algorithms: A Case Study of the Benin Electricity Community (C.E.B) |
| title_short | Short-Term Electricity Generation Forecasting Using Machine Learning Algorithms: A Case Study of the Benin Electricity Community (C.E.B) |
| title_sort | short term electricity generation forecasting using machine learning algorithms a case study of the benin electricity community c e b |
| topic | Linear regression models Short-term forecasting Electric power generation Machine Learning Algorithms |
| url | https://www.tib-op.org/ojs/index.php/th-wildau-ensp/article/view/25 |
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