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Energies
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Machine Learning in Renewable Energy Resource Assessment
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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: 20 August 2025 | Viewed by 2331

Special Issue Editors

Dr. Jin-Young Kim

E-Mail Website
Guest Editor
Renewable Energy Big Data Laboratory, Korea Institute of Energy Research, Daejeon 34129, Republic of Korea
Interests: renewable energy resource assessment; renewable energy planning and operation; climate change; measurements; numerical simulation
Special Issues, Collections and Topics in MDPI journals
Dr. Jong-Min Yeom

E-Mail Website
Guest Editor
Department of Earth and Environmental Sciences, Jeonbuk National University, Jeonju, Republic of Korea
Interests: data science; deep learning; remote sensing; solar energy; biomass energy
Special Issues, Collections and Topics in MDPI journals
Dr. Sung Goon Park

E-Mail Website
Guest Editor
Mechanical & Automotive Engineering Department, Seoul National University of Science & Technology, Seoul 01811, Republic of Korea
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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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.

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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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

29 pages, 8659 KiB  
Article
Renewable Electricity Management Cloud System for Smart Communities Using Advanced Machine Learning
by Yukta Mehta, Vincent Lo, Vijen Mehta, Kunal Agrawal, Charan Teja Madabathula, Eugene Chang and Jerry Gao
Energies 2025, 18(6), 1418; https://doi.org/10.3390/en18061418 - 13 Mar 2025
Viewed by 474
Abstract
Based on the renewable energy assessment in 2023, it was found that only 21% of total electricity is generated using renewable sources. As the global demand for electricity rises in the AI world, the need for electricity management will increase and must be [...] Read more.
Based on the renewable energy assessment in 2023, it was found that only 21% of total electricity is generated using renewable sources. As the global demand for electricity rises in the AI world, the need for electricity management will increase and must be optimized. Based on research, many companies are working on green AI electricity management, but few companies are working on predicting shortages. To identify the rising electricity demand, predict the shortage, and to bring attention to consumption, this study focuses on the optimization of solar electricity generation, tracking its consumption, and forecasting the electricity shortages well in advance. This system demonstrates a novel approach using advanced machine learning, deep learning, and reinforcement learning to maximize solar energy utilization. This paper proposes and develops a community-based model that manages and analyzes multiple buildings’ energy usage, allowing the model to perform both distributed and aggregated decision-making, achieving an accuracy of 98.2% using stacking results of models with reinforcement learning. Concerning the real-world problem, this paper provides a sustainable solution by combining data-driven models with reinforcement learning, contributing to the current market need. Full article
(This article belongs to the Special Issue Machine Learning in Renewable Energy Resource Assessment)
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17 pages, 4531 KiB  
Article
Solar Irradiance Estimation in Tropical Regions Using Recurrent Neural Networks and WRF Models
by Fadhilah A. Suwadana, Pranda M. P. Garniwa, Dhavani A. Putera, Dita Puspita, Ahmad Gufron, Indra A. Aditya, Hyunjin Lee and Iwa Garniwa
Energies 2025, 18(4), 925; https://doi.org/10.3390/en18040925 - 14 Feb 2025
Viewed by 612
Abstract
The accurate estimation of solar radiation is crucial for optimizing solar energy deployment and advancing the global energy transition. This study pioneers the development of a hybrid model combining Recurrent Neural Networks (RNNs) and the Weather Research and Forecasting (WRF) model to estimate [...] Read more.
The accurate estimation of solar radiation is crucial for optimizing solar energy deployment and advancing the global energy transition. This study pioneers the development of a hybrid model combining Recurrent Neural Networks (RNNs) and the Weather Research and Forecasting (WRF) model to estimate solar radiation in tropical regions characterized by scarce and low-quality data. Using datasets from Sumedang and Jakarta across five locations in West Java, Indonesia, the RNN model achieved moderate accuracy, with R2 values of 0.68 and 0.53 and RMSE values of 159.87 W/m2 and 125.53 W/m2, respectively. Additional metrics, such as Mean Bias Error (MBE) and relative MBE (rMBE), highlight limitations due to input data constraints. Incorporating spatially resolved GHI data from the WRF model into the RNN framework significantly enhanced accuracy under both clear and cloudy conditions, accounting for the region’s complex topography. While the results are not yet comparable to best practices in data-rich regions, they demonstrate promising potential for advancing solar radiation modeling in tropical climates. This study establishes a critical foundation for future research on hybrid solar radiation estimation techniques in challenging environments, supporting the growth of renewable energy applications in the tropics. Full article
(This article belongs to the Special Issue Machine Learning in Renewable Energy Resource Assessment)
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29 pages, 6641 KiB  
Article
ML-Enabled Solar PV Electricity Generation Projection for a Large Academic Campus to Reduce Onsite CO2 Emissions
by Sahar Zargarzadeh, Aditya Ramnarayan, Felipe de Castro and Michael Ohadi
Energies 2024, 17(23), 6188; https://doi.org/10.3390/en17236188 - 8 Dec 2024
Viewed by 857
Abstract
Mitigating CO2 emissions is essential to reduce climate change and its adverse effects on ecosystems. Photovoltaic electricity is 30 times less carbon-intensive than coal-based electricity, making solar PV an attractive option in reducing electricity demand from fossil-fuel-based sources. This study looks into [...] Read more.
Mitigating CO2 emissions is essential to reduce climate change and its adverse effects on ecosystems. Photovoltaic electricity is 30 times less carbon-intensive than coal-based electricity, making solar PV an attractive option in reducing electricity demand from fossil-fuel-based sources. This study looks into utilizing solar PV electricity production on a large university campus in an effort to reduce CO2 emissions. The study involved investigating 153 buildings on the campus, spanning nine years of data, from 2015 to 2023. The study comprised four key phases. In the first phase, PVWatts gathered data to predict PV-generated energy. This was the foundation for Phase II, where a novel tree-based ensemble learning model was developed to predict monthly PV-generated electricity. The SHAP (SHapley Additive exPlanations) technique was incorporated into the proposed framework to enhance model explainability. Phase III involved calculating historical CO2 emissions based on past energy consumption data, providing a baseline for comparison. A meta-learning algorithm was implemented in Phase IV to project future CO2 emissions post-solar PV installation. This comparison estimated a potential emissions reduction and assessed the university’s progress toward its net-zero emissions goals. The study’s findings suggest that solar PV implementation could reduce the campus’s CO2 footprint by approximately 18% for the studied cluster of buildings, supporting sustainability and cleaner energy use on the campus. Full article
(This article belongs to the Special Issue Machine Learning in Renewable Energy Resource Assessment)
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