Application of Artificial Intelligence and Machine Learning in Renewable Energy Systems

Submission Deadline: 07 May 2023 Submit to Special Issue

Guest Editors

Prof. Mohammad Hossein Ahmadi, Shahrood University of Technology, Iran.
Prof. Tingzhen Ming, Wuhan University of Technology, China.
Prof. Süheyla Yerel Kandemir, Bilecik Şeyh Edebali University, Turkey

Summary

Renewable energy is a significant player in the present global energy revolution. Parallel to this, artificial intelligence applications have emerged as a critical sector that has led to a better future for humanity. These two fields of study have collided in the recent decade. One of the most rapidly expanding topics in the contemporary period is the yielded combination. Artificial intelligence techniques, in particular, have improved the design of renewable energy systems, resulting in more sustainable goods. Improved system design, fault diagnosis, optimal operational conditions, sensitivity analysis, data analysis, decision-making, resource assessment, and exploitation of all renewable energy resources such as solar thermal, solar photovoltaic, wind energy, geothermal, and biomass are just a few of the applications. It can be used in any physical, chemical, or biological engineering application, including direct power generation, air conditioning, building heating and cooling, desalination, and energy storage.


Keywords

This special issue cordially invites academics and industrial researchers to submit cutting-edge reviews and research articles.
Potential topics include but are not limited to:
• Artificial intelligence applications in renewable energy systems (solar thermal, solar photovoltaic, wind, geothermal, biomass, and hybrid systems)
• Artificial intelligence tools for renewable resource exploration and exploitation
• Artificial intelligence applications for sustainable buildings with energy, economic, and environmental dimensions of analysis
• Artificial intelligence for fault diagnosis, operation, and maintenance of renewable energy systems
• Artificial intelligence role in the energy efficiency of renewable energy systems
• Concentrated and distributed storage systems in smart grids
• Smart metering, demand–response, and dynamic pricing
• Intelligent monitoring systems
• Control and operation for smart grids
• Smart grid impact on isolation and service restoration
• Smart grid enhancement of energy management systems
• Vehicle-to-grid (V2G)
• Data Management and Grid Analytics
• Energy management systems for microgrids
• Distributed Energy Resources distributed optimization
• Distributed Energy Resources modelling through machine learning
• Distributed Energy Resources and multi-agent system
• Distributed Energy Resources and decentralized/distributed systems
• Microgrid modelling through machine learning

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