[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118399-en":3,"doc-seo-118399-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118399,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","A Novel Hybrid Machine Learning Framework for Wind Speed Prediction","Growing environmental pressures and fossil-fuel depletion have intensified the demand for sustainable renewable electricity. Wind power is a key green option, yet forecasting is difficult because wind patterns are highly variable and uncertain. This study applies machine learning to wind power prediction by comparing K-Nearest Neighbor regression, Random Forest regression, and Support Vector regression combined with exhaustive feature selection. A hybrid framework is proposed by integrating these models with Multiple Linear Regression. Model quality is assessed using R², MAE, and RMSE on a numerically simulated dataset for a Moroccan location, and the hybrid model achieves higher prediction accuracy than individual approaches, supporting future wind-forecasting research and development.","A Novel Hybrid Machine Learning Framework for Wind Speed Prediction  \nMohamed Yassine Rhafes1*, Omar Moussaoui1, Maria Simona Raboaca2, and Traian Candin Mihaltan3  \n1MATSI Laboratory, ESTO, Mohammed First University, Oujda, Morocco  \n2ICSI Energy Department, National Research and Development Institute for Cryogenics and Isotopic Technologies, Romania  \n3Faculty of Building Services, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania  \nAbstract. 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.  \nAdditionally, 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  \nresearch and development in wind energy forecasting.  \nKeywords: Artificial Intelligence, Machine Learning, Hybrid Framework, Exhaustive Feature Selection, Wind Speed Prediction, Wind  \nEnergy  \n1 Introduction  \nIn recent years, the renewable energy sector has experienced significant growth in research and development activities, driven by the growing need for sustainable energy products and  \n* Corresponding [author:](author: mohamedyassine.rhafes@ump.ac.ma)[ ](author: mohamedyassine.rhafes@ump.ac.ma)[mohamedyassine.rhafes@ump.ac.ma](author: mohamedyassine.rhafes@ump.ac.ma)  \n© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).  \nsolutions [1] . Using renewable energy sources for electricity generation presents a viable approach to meet the increasing electricity demand and climate change challenges [2][3] .  \nElectricity generation from wind energy is challenging due to its dependence on unpredictable weather patterns [4] . Artificial intelligence, especially machine learning, has become a useful tool in the renewable energy field to address these challenges. Machine learning models excel in analyzing large volumes of data, including meteorological and geographical information, which enhances their effectiveness in managing the uncertainties of wind energy. Consequently, numerous studies have focused on improving wind energy forecasting through machine learning. Study [5] examines the use of ensemble models such as Random Forest Regression, Gradient Boosted Regression, and Extreme Gradient Boosting. Study [6] compares LASSO regression, K-Nearest Neighbor regression, XGBoost regression, and Support Vector Regression. Additionally, study [7] compares Gradient Boosting Machine, K-Nearest Neighbor Regression, Decision Tree, and Extra Tree Regression. Similarly, study [8] compares Random Forest Regression, Neural Networks","cbCaibsVxgoBjzOE","https://ap.wps.com/l/cbCaibsVxgoBjzOE","pdf",427987,1,9,"English","en",105,"# Abstract\n# 1 Introduction\n# 2 Materials and Methods\n## 2.1 Dataset\n## 2.2 Machine Learning Algorithms\n# Keywords","[{\"question\":\"Why is wind speed forecasting challenging for renewable energy systems?\",\"answer\":\"Wind power depends on weather conditions that change unpredictably, making wind patterns variable and hard to foresee accurately.\"},{\"question\":\"Which machine learning regression models are compared in the study?\",\"answer\":\"The study compares K-Nearest Neighbor regression, Random Forest regression, and Support Vector regression.\"},{\"question\":\"What makes the proposed approach “hybrid,” and how is performance evaluated?\",\"answer\":\"The hybrid approach combines the machine learning models with Multiple Linear Regression, and performance is evaluated using R², Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE).\"}]","A Novel Hybrid Machine Learning Framework for Wind Speed Prediction | 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is wind speed forecasting challenging for renewable energy systems?","Question",{"text":75,"@type":76},"Wind power depends on weather conditions that change unpredictably, making wind patterns variable and hard to foresee accurately.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning regression models are compared in the study?",{"text":80,"@type":76},"The study compares K-Nearest Neighbor regression, Random Forest regression, and Support Vector regression.",{"name":82,"@type":73,"acceptedAnswer":83},"What makes the proposed approach “hybrid,” and how is performance evaluated?",{"text":84,"@type":76},"The hybrid approach combines the machine learning models with Multiple Linear Regression, and performance is evaluated using R², Mean Absolute Error (MAE), and Root Mean Squared Error 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