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Machine Learning in Renewable Energy Resource Assessment

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "L: Energy Sources".

Deadline for manuscript submissions: 15 July 2026 | Viewed by 15036

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Special Issue Editors

Dr. Jin-Young Kim

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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
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Dr. Jong-Min Yeom

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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
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Dr. Sung Goon Park

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Guest Editor

Mechanical & Automotive Engineering Department, Seoul National University of Science & Technology, Seoul 01811, Republic of Korea
Interests: data science; CFD; wind energy
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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

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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 (9 papers)

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Research

29 pages, 12396 KB

Open AccessArticle

Multi-Channel SCADA-Based Image-Driven Power Prediction for Wind Turbines Using Optimized LeNet-5-LSTM Hybrid Neural Architecture

by Muhammad Ahsan and Phong Ba Dao

Energies 2026, 19(5), 1169; https://doi.org/10.3390/en19051169 - 26 Feb 2026

Viewed by 525

Abstract

Accurate power prediction is essential for assessing wind turbine performance under real-world operating conditions and for supporting condition monitoring and maintenance planning using SCADA data. Most existing approaches rely directly on raw SCADA signals, which may limit their ability to capture complex spatiotemporal dependencies among operational variables. To address this limitation, this paper proposes a novel SCADA-driven power prediction framework that transforms selected SCADA variables into multi-channel grayscale images and leverages an optimized LeNet-5–LSTM hybrid neural network for active and reactive power prediction. First, the SCADA dataset is analyzed to identify the most influential variables affecting power output. Six key variables are then selected, segmented, and encoded as 2D grayscale images, enabling the model to learn richer feature representations compared to conventional raw SCADA data-based methods. The proposed network combines convolutional layers for spatial feature extraction from SCADA data-based grayscale images with LSTM layers to capture temporal dependencies. Model training incorporates a customized loss function that integrates both data-driven supervision and physics-based constraints. The model is trained using 70% of the image-based dataset, with five independent runs to ensure robustness and reproducibility, while the remaining 30% is used for testing. The proposed approach is validated using SCADA data from three real-world cases: (i) a 2 MW Siemens wind turbine in Poland, (ii) a Vestas V52 wind turbine in Ireland, and (iii) the La Haute Borne wind farm in France, consisting of four wind turbines. The results demonstrate that the SCADA-based image representation enables the proposed LeNet-5–LSTM model to effectively learn discriminative feature patterns and achieve accurate active and reactive power predictions across different turbine types and operating conditions. Full article

(This article belongs to the Special Issue Machine Learning in Renewable Energy Resource Assessment)

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16 pages, 2324 KB

Open AccessArticle

FFT-Guided Multi-Window USAD with DTW–Isolation Forest for Reliable Anomaly Detection in Industrial Power Time-Series

by Woohyeon Kwon, Minsung Jung, Junseong Park and Sangkeum Lee

Energies 2025, 18(24), 6584; https://doi.org/10.3390/en18246584 - 17 Dec 2025

Cited by 1 | Viewed by 524

Abstract

Background: Industrial power time-series exhibit strong daily/weekly periodicities and nonstationary behaviors that challenge generic deep autoencoders. Methods: We take first differences of the signal, compute the FFT spectrum, and map top spectral peaks to a small set of modeling window sizes. For each window, a GELU-activated CNN–GRU autoencoder is trained under the Unsupervised Anomaly Detection (USAD) paradigm (one encoder, two decoders with an adversarial phase). Reconstruction errors are measured with Dynamic Time Warping (DTW) to mitigate phase jitter, and final anomaly decisions are obtained by fitting an Isolation Forest to the error distribution. On a three-year, single-site dataset (15 min sampling), the approach detects abrupt spikes/drops and slow drifts across sub-daily to daily rhythms; FFT-selected windows of 11, 16, 24, 32, and 96 time steps (15 min units) cover the dominant cycles. Conclusions: FFT-guided multi-window training and inference, combined with a USAD-based model, DTW-aware scoring, and Isolation Forest, yields a practical unsupervised detector for smart-factory monitoring and near-real-time deployment. Full article

(This article belongs to the Special Issue Machine Learning in Renewable Energy Resource Assessment)

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18 pages, 3577 KB

Open AccessArticle

AI-Based Mapping of Offshore Wind Energy Around the Korean Peninsula Using Sentinel-1 SAR and Numerical Weather Prediction Data

by Jason Sung-uk Joh, Son V. Nghiem, Menas Kafatos, Jay Liu, Jinsoo Kim, Seung Hee Kim and Yangwon Lee

Energies 2025, 18(23), 6252; https://doi.org/10.3390/en18236252 - 28 Nov 2025

Cited by 1 | Viewed by 989

Abstract

Offshore wind farm projects are being promoted in the seas surrounding the Korean Peninsula to secure renewable energy. To support site selection, offshore wind resource maps were generated using deep neural networks trained on Sentinel-1 SAR imagery, numerical weather prediction data, offshore wind observations, sea surface temperature, and bathymetry. The deep neural network (DNN) framework consisted of six sub-models targeting eastward and northward wind components across three regions—the Yellow Sea, Korea Strait, and East Sea—to account for spatial heterogeneity. The proposed models outperformed existing approaches, achieving mean absolute errors (MAE) ranging from 1.31 to 1.69 m/s and correlation coefficients (CC) between 0.827 and 0.913. These DNN models were then applied to produce offshore wind energy maps at a 150 m resolution, effectively capturing seasonal and regional variability. The resulting high-resolution maps provide valuable insights for evaluating the suitability of existing wind farm sites and identifying potential new candidates. Full article

(This article belongs to the Special Issue Machine Learning in Renewable Energy Resource Assessment)

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21 pages, 2623 KB

Open AccessArticle

A Cluster-Based Filtering Approach to SCADA Data Preprocessing for Wind Turbine Condition Monitoring and Fault Detection

by Krzysztof Kijanowski, Tomasz Barszcz and Phong Ba Dao

Energies 2025, 18(22), 5954; https://doi.org/10.3390/en18225954 - 12 Nov 2025

Cited by 3 | Viewed by 1179

Abstract

The high cost of wind turbine maintenance has intensified the need for reliable fault detection and condition monitoring methods. While Supervisory Control and Data Acquisition (SCADA) systems provide valuable operational data, the raw signals often contain noise, outliers, and missing or redundant entries, which can compromise analysis accuracy. This study presents a novel cluster-based outlier removal approach for SCADA data preprocessing, featuring a unique flexibility to include or exclude negative power values—a factor rarely investigated but potentially critical for fault detection performance. The method applies the K-Means++ unsupervised clustering algorithm to group data points along the wind speed–power curve. The number of clusters is determined heuristically using the elbow method, while outliers are identified through Mahalanobis distance with thresholds derived from Chebyshev’s inequality theorem. The approach was validated using SCADA data from a wind farm in Portugal and further assessed with a CUSUM test-based structural change detection method to study how preprocessing choices—outlier thresholds (5% vs. 1%) and inclusion/exclusion of negative power values—affect early fault identification. Results demonstrate reliable fault detection up to 14 days before failure, retaining over 99% of the original dataset. This work provides key insights into preprocessing impacts on model reliability and offers an open-source Python implementation for reproducibility. Full article

(This article belongs to the Special Issue Machine Learning in Renewable Energy Resource Assessment)

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18 pages, 6011 KB

Open AccessFeature PaperArticle

From Data-Rich to Data-Scarce: Spatiotemporal Evaluation of a Hybrid Wavelet-Enhanced Deep Learning Model for Day-Ahead Wind Power Forecasting Across Greece

by Ioannis Laios, Dimitrios Zafirakis and Konstantinos Moustris

Energies 2025, 18(21), 5585; https://doi.org/10.3390/en18215585 - 24 Oct 2025

Cited by 1 | Viewed by 1187

Abstract

Efficient wind power forecasting is critical in achieving large-scale integration of wind energy in modern electricity systems. On the other hand, limited availability of wealthy, long-term historical data of wind power generation for many sites of interest often challenges the training of tailored forecasting models, which, in turn, introduces uncertainty concerning the anticipated operational status of similar early-life, or even prospective, wind farm projects. To that end, this study puts forward a spatiotemporal, national-level forecasting exercise as a means of addressing wind power data scarcity in Greece. It does so by developing a hybrid wavelet-enhanced deep learning model that leverages long-term historical data from a reference site located in central Greece. The model is optimized for 24-h day-ahead forecasting, using a hybrid architecture that incorporates discrete wavelet transform for feature extraction, with deep neural networks for spatiotemporal learning. Accordingly, the model’s generalization is evaluated across a number of geographically distributed sites of different quality wind potential, each constrained to only one year of available data. The analysis compares forecasting performance between the original and target sites to assess spatiotemporal robustness of the model without site-specific retraining. Our results demonstrate that the developed model maintains competitive accuracy across data-scarce locations for the first 12 h of the day-ahead forecasting horizon, designating, at the same time, distinct performance patterns, dependent on the geographical and wind potential quality dimensions of the examined areas. Overall, this work underscores the feasibility of leveraging data-rich regions to inform forecasting in under-instrumented areas and contributes to the broader discourse on spatial generalization in renewable energy modeling and planning. Full article

(This article belongs to the Special Issue Machine Learning in Renewable Energy Resource Assessment)

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19 pages, 4241 KB

Open AccessArticle

A Comparative Study of Customized Algorithms for Anomaly Detection in Industry-Specific Power Data

by Minsung Jung, Hyeonseok Jang, Woohyeon Kwon, Jiyun Seo, Suna Park, Beomdo Park, Junseong Park, Donggeon Yu and Sangkeum Lee

Energies 2025, 18(14), 3720; https://doi.org/10.3390/en18143720 - 14 Jul 2025

Cited by 3 | Viewed by 1632

Abstract

This study compares and analyzes statistical, machine learning, and deep learning outlier-detection methods on real power-usage data from the metal, food, and chemical industries to propose the optimal model for improving energy-consumption efficiency. In the metal industry, a Z-Score-based statistical approach with threshold optimization was used; in the food industry, a hybrid model combining K-Means, Isolation Forest, and Autoencoder was designed; and in the chemical industry, the DBA K-Means algorithm (Dynamic Time Warping Barycenter Averaging) was employed. Experimental results show that the Isolation Forest–Autoencoder hybrid delivers the best overall performance, and that DBA K-Means excels at detecting seasonal outliers, demonstrating the efficacy of these algorithms for smart energy-management systems and carbon-neutral infrastructure Full article

(This article belongs to the Special Issue Machine Learning in Renewable Energy Resource Assessment)

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29 pages, 8659 KB

Open AccessArticle

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

Cited by 3 | Viewed by 1702

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 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 KB

Open AccessArticle

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

Cited by 3 | Viewed by 3643

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 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 KB

Open AccessEditor’s ChoiceArticle

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

Cited by 3 | Viewed by 2041

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 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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