Predicting Wind Energy: Machine Learning from Daily Wind Data

Title

Predicting Wind Energy: Machine Learning from Daily Wind Data

Creator

Subramani K.; Sharon Sophia J.; Habelalmateen M.I.; Singh R.; Pahade A.; Ikhar S.

Description

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. The Authors, published by EDP Sciences, 2024.

Source

E3S Web of Conferences, Vol-540

Date

2024-01-01

Publisher

EDP Sciences

Coverage

Subramani K., School of Business and Management, CHRIST(Deemed to Be University) Bangalore Yeshwantpur Campus, India; Sharon Sophia J., School of Business and Management, CHRIST(Deemed to Be University) Bangalore Yeshwantpur Campus, India; Habelalmateen M.I., The Islamic University, Najaf, Iraq; Singh R., Department of Electronics and Communication Engineering, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun, 248007, India; Pahade A., Department of Computer Science and Engineering, IES College of Technology, IES University, Madhya Pradesh, Bhopal, 462044, India; Ikhar S., Yashika Journal Publications Pvt Ltd, Maharashtra, Wardha, India

Rights

All Open Access; Gold Open Access

Relation

ISSN: 25550403

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1051/e3sconf/202454003009

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198130852&doi=10.1051%2fe3sconf%2f202454003009&partnerID=40&md5=5b6e72985b2c1e9bac6948b40b55e6ee