Machine Learning and the Renewable Energy Transition

Dear Colleagues,

This Special Issue focuses on how advanced algorithms and data-driven insights can accelerate the shift toward sustainable energy systems at the junction of ML and renewable energy transition. It thereby addresses a wide scope of topics, from the optimization of renewable energy sources, predictive maintenance for energy infrastructure, and energy demand forecasting to the integration of distributed energy resources. It will cover the following areas, which have the potential to prove the capability of machine learning towards the solution of complex challenges in energy production, distribution, and consumption.

This Special Issue is an attempt to give a comprehensive overview of current research in cutting-edge work and applications of machine learning in the renewable energy sector. Second, it tries to fill gaps in the literature through the selected innovative methodologies and case studies that give vivid depictions of the practical impact of ML on improving energy efficiency and reliability. Moreover, this Special Issue will form a kind of bridge between the artificial intelligence and sustainable energy communities, thus promoting cross-disciplinary collaboration and motivating further research.

Dr. Esposito Luca
Dr. Annamaria Nese
Dr. Anna Parziale
Dr. Xihui Chen
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• machine learning
• renewable energy
• energy transition
• predictive maintenance
• energy optimization
• demand forecasting
• distributed energy resources
• sustainability
• artificial intelligence
• energy efficiency