[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120020-en":3,"doc-seo-120020-105":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":20,"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},120020,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Predicting Wind Energy - Machine Learning from Daily Wind Data","The paper reviews progress in renewable energy forecasting, emphasizing wind energy and the need for accurate predictions to support efficient energy management and grid integration. It highlights how machine learning and deep learning approaches can learn from historical wind speed, direction, and production data to improve forecast accuracy. Methods such as Support Vector Regression and Random Forest Regression are presented as promising. It also examines limitations of traditional statistical and physical models, and discusses key challenges including uncertainty, data availability, and interpretability while aiming toward dependable renewable integration.","Predicting Wind Energy: Machine Learning from Daily Wind Data  \nDr K Subramani 1, Dr. Sharon Sophia. J2, Mohammed I. Habelalmateen3, Rajesh Singh4, Akhilesh Pahade5, and Sharayu Ikhar6  \n*Assistant Professor ,School of Business and Management , CHRIST(Deemed to be University ) Bangalore Yeshwantpur Campus  \n†Assistant Professor,School of Business and Management, CHRIST (Deemed to be University) Bangalore Yeshwantpur Campus  \n‡The Islamic university, Najaf, Iraq  \n§Department of Electronics & Communication engineering  \nUttaranchal Institute of Technology, Uttaranchal University, Dehradun-248007, India  \n**Department of Computer Science & Engineering, IES College of Technology, IES University, Bhopal, Madhya Pradesh 462044 India.  \n6Researcher, Yashika Journal Publications Pvt Ltd, Wardha, Maharashtra, India Email: [sharyu.ikhar@gmail.com](sharyu.ikhar@gmail.com)  \nAbstract.This paper offers a comprehensive review of the advancementsin the realm of renewable energy, specifically focusing on solid oxide fuel cells and electrolysers for green hydrogen production. The review delves into the significance of wind energy as a pivotal renewable energy source and underscores the importance of precise forecasting for efficient energy management and distribution. The integration of machine learning-based approaches, such as Support Vector Regression and Random Forest Regression, has shown promising results in enhancing the accuracy of wind energy production forecasts. Furthermore, the paper explores the broader landscape of renewable energy generation forecasting, emphasizing the rising prominence of machine learning and deep learning techniques. As the penetration of renewable energy sources into the  \n*Corresponding Authour :[Subramani.k@christuniversity.in](Subramani.k@christuniversity.in)  \n†[sharon.sophia@christuniversity.in](sharon.sophia@christuniversity.in)  \n‡[mohammed.ha@iunajaf.edu.iq](mohammed.ha@iunajaf.edu.iq)  \n§[drrajeshsingh004@gmail.com](drrajeshsingh004@gmail.com)  \n**[research@iesbpl.ac.in](research@iesbpl.ac.in)  \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/)).  \nelectricity grid intensifies, the need for accurate forecasting becomes paramount. Traditional methods, while valuable, have encountered limitations, paving the way for advanced algorithms capable of deciphering intricate data relationships. The review also touches upon the inherent challenges and prospective research avenues in the domain, including addressing uncertainties in renewable energy generation, ensuring data availability, and enhancing model interpretability. The overarching goal remains the seamless integration of renewable sources into the grid,  \npropelling us towards a greener future.  \n1 Introduction  \nThe global energy landscape is undergoing a transformative shift, with renewable energy sources (RES) playing a pivotal role in steering the world towards a sustainable future. Wind energy, in particular, has risen to prominence as a vital renewable energy source. Its rapid adoption is a testament to its potential; however, the intermittent nature of wind energy poses challenges in accurately forecasting its production. The unpredictability stemming from variable wind speeds and directions necessitates innovative solutions to ensure consistent and efficient energy generation.  \nMachine learning (ML) and deep learning (DL) have emerged as frontrunners in addressing these challenges. By harnessing historical data on wind speed, direction, and energy production, these algorithms can discern patterns and make informed predictions. Such accurate forecasts are indispensable for the optimal planning and management of wind farms, ensuring a harmonious balance between energy supply and demand. Furthermore , the integration of ML and DL in renewable energy for","cbCaijVBVOVySM40","https://ap.wps.com/l/cbCaijVBVOVySM40","pdf",1578236,1,9,"English","en",105,"# Abstract\n# 1 Introduction\n## Renewable energy transition and wind intermittency\n## ML/DL approaches for forecasting\n## Traditional models and their limitations\n## Aim of the review\n# 2 Review and discussion","[{\"question\":\"Why is accurate wind energy forecasting important in renewable integration?\",\"answer\":\"Accurate forecasts enable efficient planning and management of wind farms and help balance energy supply with demand as renewable penetration increases.\"},{\"question\":\"Which machine learning methods are highlighted for improving wind production forecasts?\",\"answer\":\"Support Vector Regression and Random Forest Regression are discussed as approaches that show promise in enhancing forecast accuracy.\"},{\"question\":\"What limitations are associated with traditional forecasting models?\",\"answer\":\"Traditional statistical and physical models struggle with the nonlinear and uncertain nature of renewable energy data and weather-related variability, motivating advanced ML/DL methods.\"}]","Predicting Wind Energy - 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