Machine Learning in Renewable Energy Resource Assessment
This special issue belongs to the section "L: Energy Sources".
Special Issue Information
Dear Colleagues,
It is difficult to imagine future energy sources that are not renewable. Their use has been increasing not only for national security reasons but also for sustainable environments. Renewable energy assessment has contributed to the advances in the energy integration process among space and energy planning at the local, regional, and national scale.
We are pleased to invite you to this Special Issue, which will focus on the application of machine learning (ML) techniques in the assessment of renewable energy resources. It will explore the transformative potential of data-driven methodologies across a wide range of renewable energy technologies, including, but not limited to, solar energy, wind energy, hydrogen and fuel cells, bioenergy, geothermal energy, hydropower, marine energy, and renewable energy integration systems.
This Special Issue aims to bridge the gap between ML advancements and renewable energy technologies, offering insights into how AI and machine learning can enhance accuracy, efficiency, and sustainability in resource assessment and energy system planning.
Dr. Jin-Young Kim
Dr. Jong-Min Yeom
Dr. Sung Goon Park
Guest Editors
Manuscript Submission Information
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Keywords
- machine learning applications in renewable energy resource assessment
- advanced ML techniques for renewable energy forecasting
- data-driven models for wind, solar, hydrogen, bioenergy, geothermal, hydropower, and marine energy production as well as optimization
- machine learning for integrating renewable resources into energy grids
- policy, strategy, and low-carbon technology through AI/ML
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Published Papers
Energies - ISSN 1996-1073