Machine Learning in Renewable Energy Resource Assessment
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "L: Energy Sources".
Deadline for manuscript submissions: 25 April 2025 | Viewed by 45
Special Issue Editors
Interests: renewable energy resource assessment; renewable energy planning and operation; climate change; measurements; numerical simulation
Special Issues, Collections and Topics in MDPI journals
Interests: data science; deep learning; remote sensing; solar energy; biomass energy
Special Issues, Collections and Topics in MDPI journals
Interests: data science; CFD; wind energy
Special Issues, Collections and Topics in MDPI journals
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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