Predicting Wind Energy: Machine Learning from Daily Wind Data

^* Assistant Professor, School of Business and Management, CHRIST(Deemed to be University ) Bangalore Yeshwantpur Campus
^† Assistant Professor, School of Business and Management, CHRIST (Deemed to be University) Bangalore Yeshwantpur Campus
^‡ The Islamic university, Najaf, Iraq
^§ Department of Electronics & Communication engineering Uttaranchal Institute of Technology, Uttaranchal University, Dehradun-248007, India
^** Department of Computer Science & Engineering, IES College of Technology, IES University, Madhya Pradesh 462044 India, Bhopal .
⁶ Researcher, Yashika Journal Publications Pvt Ltd, India Email: sharyu.ikhar@gmail.com, Wardha, Maharashtra

^* Corresponding Author :Subramani.k@christuniversity.in
^† sharon.sophia@christuniversity.in
^‡ mohammed.ha@iunajaf.edu.iq
^§ drrajeshsingh004@gmail.com
^** research@iesbpl.ac.in

Abstract

This paper offers a comprehensive review of the advancements in 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 electricity 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, propelling us towards a greener future.