Guest Editors
Ir. Dr. Hadi Nabipour Afrouz
Email: Hadi.nabipourafrouzi@bcu.ac.uk
Affiliation: College of Engineering, Faculty of Computing, Engineering and the Built Environment, Birmingham City University, Birmingham B4 7XG, West Midlands, England, United Kingdom
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Research Interests: Renewable energy (Sizing, Optimization, Hybrid System); Renewable Energy Economics and Policy; Environmental and social impacts of renewable energy systems
Dr. Md Bazlul Mobin Siddique
Email: msiddique@swinburne.edu.my
Affiliation: Department of Science, Faculty of Engineering Computing and Science, Swinburne University of Technology Sarawak Campus, 93350 Kuching, Sarawak, Malaysia
Homepage:
Research Interests:Biofuel; Biowaste to Biocomposite; Fats and Oils
Dr. Mujahid Tabassum
Email: Mujahid.tabassum@setu.ie
Affiliation: South East Technological University, Ireland
Homepage:
Research Interests: AI and Machine Learning; Communications; Renewable
Summary
This special issue focuses on the intersection of renewable energy systems and machine learning, exploring how intelligent algorithms can be leveraged to enhance the performance, efficiency, and optimization of sustainable energy technologies. The rapid growth of renewable energy sources such as solar, wind, and hydropower requires advanced control, forecasting, and optimization methods to meet the increasing demand for reliable, cost-effective power generation.
Machine learning, with its ability to analyze large datasets, identify patterns, and predict outcomes, offers promising solutions to key challenges in renewable energy. This special issue invites contributions that address innovative applications of machine learning in areas like energy forecasting, grid stability, predictive maintenance, and system optimization. It aims to highlight how these technologies can improve the overall efficiency of energy systems, reduce costs, and contribute to the global shift toward cleaner, more sustainable energy.
By bringing together experts from both fields, this issue will provide insights into cutting-edge developments and offer a roadmap for future research in the integration of machine learning with renewable energy systems.
Keywords
Renewable Energy, Machine Learning, Energy Optimization, Artificial Intelligence (AI), Energy Forecasting, Smart Grids, Predictive Maintenance, Energy Efficiency, Grid Stability, Data-Driven Energy Solutions, Energy Management, Renewable Energy Integration
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