Issue |
E3S Web Conf.
Volume 601, 2025
The 3rd International Conference on Energy and Green Computing (ICEGC’2024)
|
|
---|---|---|
Article Number | 00067 | |
Number of page(s) | 9 | |
DOI | https://doi.org/10.1051/e3sconf/202560100067 | |
Published online | 16 January 2025 |
A Novel Hybrid Machine Learning Framework for Wind Speed Prediction
1 MATSI Laboratory, ESTO, Mohammed First University, Oujda, Morocco
2 ICSI Energy Department, National Research and Development Institute for Cryogenics and Isotopic Technologies, Romania
3 Faculty of Building Services, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania
* Corresponding author: mohamedyassine.rhafes@ump.ac.ma
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.
Key words: Artificial Intelligence / Machine Learning / Hybrid Framework / Exhaustive Feature Selection / Wind Speed Prediction / Wind Energy
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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