A Novel Hybrid Machine Learning Framework for Wind Speed Prediction
The growing urgency of environmental challenges and the depletion of fossil fuels have accelerated the search for sustainable and renewable energy sources. Wind energy, for example, is an important source of green electricity. However, using wind power is challenging due to the variability and unpre...
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| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | E3S Web of Conferences |
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| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/01/e3sconf_icegc2024_00067.pdf |
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| author | Rhafes Mohamed Yassine Moussaoui Omar Raboaca Maria Simona Mihaltan Traian Candin |
| author_facet | Rhafes Mohamed Yassine Moussaoui Omar Raboaca Maria Simona Mihaltan Traian Candin |
| author_sort | Rhafes Mohamed Yassine |
| collection | DOAJ |
| description | The growing urgency of environmental challenges and the depletion of fossil fuels have accelerated the search for sustainable and renewable energy sources. Wind energy, for example, is an important source of green electricity. However, using wind power is challenging due to the variability and unpredictability of wind patterns. Consequently, the ability to predict wind power in advance is crucial. The integration of artificial intelligence within the renewable energy sector could provide a viable solution to this challenge. In this study, we investigate the potential of machine learning to improve wind power forecasting by conducting a comparison of three regression models: K-Nearest Neighbor regression, Random Forest regression, and Support Vector regression. These models are combined with a feature selection technique to forecast wind power. Additionally, we propose a novel hybrid approach that combines these machine learning models with Multiple Linear Regression to address the complexities of wind energy forecasting. The performance of the models is evaluated using the R² score, Mean Absolute Error, and Root Mean Squared Error. The dataset for this study was generated from a numerical simulation conducted at a location with a latitude of 22.55° N and a longitude of -14.33° E. The findings demonstrate that the proposed hybrid model outperforms the individual machine learning models in terms of prediction accuracy. This study provides a solid foundation for future research and development in wind energy forecasting. |
| format | Article |
| id | doaj-art-dd024ad782ea41c6b10e2a43ce95d0ae |
| institution | Kabale University |
| issn | 2267-1242 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj-art-dd024ad782ea41c6b10e2a43ce95d0ae2025-02-05T10:46:25ZengEDP SciencesE3S Web of Conferences2267-12422025-01-016010006710.1051/e3sconf/202560100067e3sconf_icegc2024_00067A Novel Hybrid Machine Learning Framework for Wind Speed PredictionRhafes Mohamed Yassine0Moussaoui Omar1Raboaca Maria Simona2Mihaltan Traian Candin3MATSI Laboratory, ESTO, Mohammed First UniversityMATSI Laboratory, ESTO, Mohammed First UniversityICSI Energy Department, National Research and Development Institute for Cryogenics and Isotopic TechnologiesFaculty of Building Services, Technical University of Cluj-NapocaThe growing urgency of environmental challenges and the depletion of fossil fuels have accelerated the search for sustainable and renewable energy sources. Wind energy, for example, is an important source of green electricity. However, using wind power is challenging due to the variability and unpredictability of wind patterns. Consequently, the ability to predict wind power in advance is crucial. The integration of artificial intelligence within the renewable energy sector could provide a viable solution to this challenge. In this study, we investigate the potential of machine learning to improve wind power forecasting by conducting a comparison of three regression models: K-Nearest Neighbor regression, Random Forest regression, and Support Vector regression. These models are combined with a feature selection technique to forecast wind power. Additionally, we propose a novel hybrid approach that combines these machine learning models with Multiple Linear Regression to address the complexities of wind energy forecasting. The performance of the models is evaluated using the R² score, Mean Absolute Error, and Root Mean Squared Error. The dataset for this study was generated from a numerical simulation conducted at a location with a latitude of 22.55° N and a longitude of -14.33° E. The findings demonstrate that the proposed hybrid model outperforms the individual machine learning models in terms of prediction accuracy. This study provides a solid foundation for future research and development in wind energy forecasting.https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/01/e3sconf_icegc2024_00067.pdfartificial intelligencemachine learninghybrid frameworkexhaustive feature selectionwind speed predictionwind energy |
| spellingShingle | Rhafes Mohamed Yassine Moussaoui Omar Raboaca Maria Simona Mihaltan Traian Candin A Novel Hybrid Machine Learning Framework for Wind Speed Prediction E3S Web of Conferences artificial intelligence machine learning hybrid framework exhaustive feature selection wind speed prediction wind energy |
| title | A Novel Hybrid Machine Learning Framework for Wind Speed Prediction |
| title_full | A Novel Hybrid Machine Learning Framework for Wind Speed Prediction |
| title_fullStr | A Novel Hybrid Machine Learning Framework for Wind Speed Prediction |
| title_full_unstemmed | A Novel Hybrid Machine Learning Framework for Wind Speed Prediction |
| title_short | A Novel Hybrid Machine Learning Framework for Wind Speed Prediction |
| title_sort | novel hybrid machine learning framework for wind speed prediction |
| topic | artificial intelligence machine learning hybrid framework exhaustive feature selection wind speed prediction wind energy |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/01/e3sconf_icegc2024_00067.pdf |
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