Machine learning solutions for renewable energy systems: Applications, challenges, limitations, and future directions

• Published in: Journal of environmental management 2024-03, Vol.354, p.120392-120392, Article 120392
• Main Authors: Allal, Zaid, Noura, Hassan N., Salman, Ola, Chahine, Khaled
• Format: Article
• Language: English
• Subjects: Algorithms; Cyber attacks; Demand forecasting; energy; Energy storage; environmental management; Fault diagnosis; Global Warming; Grid stability; Machine Learning; Paris; Renewable Energy; renewable energy sources; Resource assessment; United Nations Framework Convention on Climate Change
• ISSN: 0301-4797; 1095-8630
• DOI: 10.1016/j.jenvman.2024.120392

The Paris Agreement, a landmark international treaty signed in 2016 to limit global warming to 2°C, has urged researchers to explore various strategies for achieving its ambitious goals. While Renewable Energy (RE) innovation holds promise, it alone may not be sufficient as critical deadlines approach. This field of research presents numerous challenges, foremost among them being the costliness of materials involved. However, emerging advancements in Machine Learning (ML) technologies provide a glimmer of hope; these sophisticated algorithms can accurately predict the output of energy systems without relying on physical resources and instead leverage available data from diverse energy platforms that have emerged over recent decades. The primary objective of this paper is to comprehensively explore various ML techniques and algorithms in the context of Renewable Energy Systems (RES). The investigation will address several vital inquiries, including identifying and evaluating existing RE technologies, assessing their potential for further advancement, and thoroughly analyzing the challenges and limitations associated with their deployment and testing. Furthermore, this research examines how ML can effectively overcome these obstacles by enhancing RES performance. By identifying future research opportunities and outlining potential directions for improvement, this work seeks to contribute to developing environmentally sustainable energy systems. •Discussed the advantages and limitations of current renewable energy systems.•Reviewed the different machine learning algorithms and their applications in renewable energy systems.•Highlighted existing renewable energy datasets and feature selection methods.•Discussed the use of machine learning and renewable energy in the hydrogen value chain.•Provided future directions for the application of machine learning in the renewable energy sector.