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Applied Sciences
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New Trends in Renewable Energy and Power Systems
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New Trends in Renewable Energy and Power Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: 20 May 2025 | Viewed by 2467

Special Issue Editors

Dr. Monica Borunda

E-Mail Website
Guest Editor
1. Centro Nacional de Investigación y Desarrollo Tecnológico, Tecnológico Nacional de México, Cuernavaca 62490, Mexico
2. Consejo Nacional de Humanidades, Ciencias y Tecnologías, Mexico City 03940, Mexico
3. Faculty of Science, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
Interests: renewable energy; energy generation; energy management; artificial intelligence
Dr. Oscar A. Jaramillo

E-Mail Website
Guest Editor
Instituto de Energías Renovables, Universidad Nacional Autónoma de México, Temixco 62580, Mexico
Interests: solar energy; wind energy; renewable energy; power systems

Special Issue Information

Dear Colleagues,

The renewable energy sector is rapidly evolving towards more sustainable, efficient and resilient energy systems driven by innovation, policy, and the compelling need to address climate change. Thus, renewable energy and power systems are experiencing hasty advances due to technological innovations, policy changes, and increasing environmental awareness.

The most notable emerging trends shaping the future of power systems involve novel small and large-scale photovoltaic and wind energy systems, energy storage, green hydrogen, distributed energy systems, microgrids, digitalization of energy systems, smart energy systems, AI-assisted energy systems, carbon capture and utilization, electric vehicles, emerging technologies, and sustainability and circular economy.

These developments are important since they aim to transform the global energy landscape, aiming to (a) reduce greenhouse gas emissions to meet climate goals and preserve our Earth and its natural resources, (b) provide energy security and independence hand in hand with economic benefits and public health for various populations, and (c) drive technological innovation to achieve sustainable development. Embracing these trends is essential for building a sustainable, resilient, and equitable energy future.

This Special Issue seeks to publish high-quality, cutting-edge innovative papers related to novel trends regarding renewable energy and power systems.

Dr. Monica Borunda
Dr. Oscar A. Jaramillo
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. Applied Sciences 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 2400 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

  • energy
  • renewable energy
  • wind energy
  • solar energy
  • smart energy systems
  • energy tools and models
  • energy storage
  • clean energy sources
  • carbon capture
  • electromobility
  • sustainability
  • artificial intelligence

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Published Papers (4 papers)

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Research

34 pages, 42694 KiB  
Article
SPHERE: Benchmarking YOLO vs. CNN on a Novel Dataset for High-Accuracy Solar Panel Defect Detection in Renewable Energy Systems
by Kubilay Ayturan, Berat Sarıkamış, Mehmet Feyzi Akşahin and Uğurhan Kutbay
Appl. Sci. 2025, 15(9), 4880; https://doi.org/10.3390/app15094880 - 28 Apr 2025
Viewed by 196
Abstract
Solar panels are critical for renewable electricity generation, yet defects significantly reduce power output and risk grid instability, necessitating reliable AI-driven defect detection. We propose the SPHERE (Solar Panel Hidden-Defect Evaluation for Renewable Energy) method for such cases. This study compares deep learning [...] Read more.
Solar panels are critical for renewable electricity generation, yet defects significantly reduce power output and risk grid instability, necessitating reliable AI-driven defect detection. We propose the SPHERE (Solar Panel Hidden-Defect Evaluation for Renewable Energy) method for such cases. This study compares deep learning models for classifying solar panel images (broken, clean, and dirty) using a novel, proprietary dataset of 6079 images augmented to enhance performance. The following three models were evaluated: YOLOv8-m, YOLOv9-e, and a custom CNN with 9-fold cross-validation. Pre-trained models (e.g., VGG16 and ResNet) were assessed but outperformed by YOLO variants. Metrics included accuracy, precision–recall, F1-score, sensitivity, and specificity. YOLOv8-m achieved the highest accuracy (97.26%) and specificity (95.94%) with 100% sensitivity, excelling in defect identification. YOLOv9-e showed slightly lower accuracy (95.18%) but maintained high sensitivity. The CNN model demonstrated robust generalization (92.86% accuracy) via cross-validation, though it underperformed relative to YOLO architectures. Results highlight YOLO-based models’ superiority, particularly YOLOv8-m, in balancing precision and robustness for this classification task. This study underscores the potential of YOLO frameworks in automated solar panel inspection systems, offering enhanced maintenance and grid stability reliability. This contributes to advancing AI applications in renewable energy infrastructure, ensuring efficient defect detection and sustained power output. The dataset’s novelty and the models’ comparative analysis provide a foundation for future research in autonomous maintenance solutions. Full article
(This article belongs to the Special Issue New Trends in Renewable Energy and Power Systems)
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32 pages, 6835 KiB  
Article
An Intelligent Method for Day-Ahead Regional Load Demand Forecasting via Machine-Learning Analysis of Energy Consumption Patterns Across Daily, Weekly, and Annual Scales
by Monica Borunda, Arturo Ortega Vega, Raul Garduno, Luis Conde, Manuel Adam Medina, Jeannete Ramírez Aparicio, Lorena Magallón Cacho and O. A. Jaramillo
Appl. Sci. 2025, 15(9), 4717; https://doi.org/10.3390/app15094717 - 24 Apr 2025
Viewed by 130
Abstract
Electric power load forecasting is essential for the efficient operation and strategic planning of utilities. Decisions regarding the electric market, power generation, load management, and infrastructure development all rely on accurate load predictions. This work presents a novel methodology for day-ahead load forecasting. [...] Read more.
Electric power load forecasting is essential for the efficient operation and strategic planning of utilities. Decisions regarding the electric market, power generation, load management, and infrastructure development all rely on accurate load predictions. This work presents a novel methodology for day-ahead load forecasting. The approach employs a long short-term memory neural network (LSTM NN) trained on representative load and meteorological data from the region. Before training, the load dataset is grouped by its statistical seasonality through K-means clustering analysis. Clustering load demand, along with similar-day data management, enables more focused training of the LSTM network on uniform data subsets, enhancing the model’s ability to capture temporal patterns and reducing the complexity associated with high variability in demand data. A case study using hourly load demand time-series data provided by the Centro Nacional de Control de Energía (CENACE) is analyzed, and the mean absolute percentage error (MAPE) is calculated, showing lower MAPE than traditional methods. This hybrid approach demonstrates the potential of integrating clustering techniques with neural networks and representative meteorological data from the region to achieve more reliable and accurate regional day-ahead load forecasting. Full article
(This article belongs to the Special Issue New Trends in Renewable Energy and Power Systems)
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24 pages, 2817 KiB  
Article
Risk-Based Optimization of Renewable Energy Investment Portfolios: A Multi-Stage Stochastic Approach to Address Uncertainty
by Olufemi Ogunniran, Olubayo Babatunde, Busola Akintayo, Kolawole Adisa, Desmond Ighravwe, John Ogbemhe and Oludolapo Akanni Olanrewaju
Appl. Sci. 2025, 15(5), 2346; https://doi.org/10.3390/app15052346 - 22 Feb 2025
Viewed by 799
Abstract
This work presents a multi-stage stochastic optimization model intended to improve investment decision-making for energy projects by incorporating uncertainty in contexts and changes in market pricing. In contrast to conventional deterministic models, which generally concentrate on a singular stage while neglecting the intricacies [...] Read more.
This work presents a multi-stage stochastic optimization model intended to improve investment decision-making for energy projects by incorporating uncertainty in contexts and changes in market pricing. In contrast to conventional deterministic models, which generally concentrate on a singular stage while neglecting the intricacies associated with policy and market uncertainties, our methodology incorporates Conditional Value at Risk as a pivotal risk metric. Across a span of five years, the model predicts how investments will be distributed among three types of electricity projects: Solar Farm, Wind Farm, and Hydro Plant. The stochastic model strategically allocates an investment of USD 16.5 million to achieve an expansion in the capacity of 925 megawatts and an expected portfolio return of USD 1,822,500. Notably, the model maintains a Conditional Value at Risk of USD 100,000 and an impressive Sharpe Ratio of 18.2250, demonstrating its ability to offer improved risk-adjusted returns. This study illustrates the effectiveness of stage stochastic optimization in enhancing diverse and robust renewable energy portfolios. Full article
(This article belongs to the Special Issue New Trends in Renewable Energy and Power Systems)
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19 pages, 2451 KiB  
Article
Testing the Wind Energy Data Based on Environmental Factors Predicted by Machine Learning with Analysis of Variance
by Yasemin Ayaz Atalan and Abdulkadir Atalan
Appl. Sci. 2025, 15(1), 241; https://doi.org/10.3390/app15010241 - 30 Dec 2024
Viewed by 976
Abstract
This study proposes a two-stage methodology for predicting wind energy production using time, environmental, technical, and locational variables. In the first stage, machine learning algorithms, including random forest (RF), gradient boosting (GB), k-nearest neighbors (kNNs), linear regression (LR), and decision trees (Tree), were [...] Read more.
This study proposes a two-stage methodology for predicting wind energy production using time, environmental, technical, and locational variables. In the first stage, machine learning algorithms, including random forest (RF), gradient boosting (GB), k-nearest neighbors (kNNs), linear regression (LR), and decision trees (Tree), were employed to estimate energy output. Among these, RF exhibited the best performance with the lowest error metrics (MSE: 0.003, RMSE: 0.053) and the highest R2 value (0.988). In the second stage, analysis of variance (ANOVA) was conducted to evaluate the statistical relationships between independent variables and the predicted dependent variable, identifying wind speed (p < 0.001) and rotor speed (p < 0.001) as the most influential factors. Furthermore, RF and GB models produced predictions most closely aligned with actual data, achieving R2 values of 88.83% and 89.30% in the ANOVA validation phase. Integrating RF and GB models with statistical validation highlighted the robustness of the methodology. These findings demonstrate the robustness of integrating machine learning models with statistical verification methods. Full article
(This article belongs to the Special Issue New Trends in Renewable Energy and Power Systems)
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