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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 July 2026 | Viewed by 30304

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Special Issue Editors

Dr. Monica Borunda

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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

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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 (9 papers)

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Research

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24 pages, 6667 KB

Open AccessArticle

Data-Driven Forecasting of Electricity Prices in Chile Using Machine Learning

by Ricardo León, Guillermo Ramírez, Camilo Cifuentes, Samuel Vergara, Roberto Aedo-García, Francisco Ramis Lanyon and Rodrigo J. Villalobos San Martin

Appl. Sci. 2026, 16(3), 1318; https://doi.org/10.3390/app16031318 - 28 Jan 2026

Viewed by 384

Abstract

This study proposes and evaluates a data-driven framework for short-term System Marginal Price (SMP) forecasting in the Chilean National Electric System (NES), a power system characterized by high penetration of variable renewable generation and persistent transmission congestion. Using publicly available hourly operational data for 2024, multiple machine learning regressors including Linear Regression (base case), Bayesian Ridge, Automatic Relevance Determination, Decision Trees, Random Forests, and Support Vector Regression are implemented under a node-specific modeling strategy. Two alternative approaches for predictor selection are compared: a system-wide methodology that exploits lagged SMP information from all network nodes; and a spatially filtered methodology that restricts SMP inputs to correlated subsystems identified through nodal correlation analysis. Model robustness is explicitly assessed by reserving January and July as out-of-sample test periods, capturing contrasting summer and winter operating conditions. Forecasting performance is analyzed for representative nodes located in the northern, central, and southern zones of the NES, which exhibit markedly different congestion levels and generation mixes. Results indicate that non-linear and ensemble models, particularly Random Forest and Support Vector Regression, provide the most accurate forecasts in well-connected areas, achieving mean absolute errors close to 10 USD/MWh. In contrast, forecast errors increase substantially in highly congested southern zones, reflecting the structural influence of transmission constraints on price formation. While average performance differences between M1 and M2 are modest, a paired Wilcoxon signed-rank test reveals statistically significant improvements with M2 in highly congested zones, where M2 yields lower absolute errors for most models, despite relying on fewer inputs. These findings highlight the importance of congestion-aware feature selection for reliable price forecasting in renewable-intensive systems. Full article

(This article belongs to the Special Issue New Trends in Renewable Energy and Power Systems)

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20 pages, 4285 KB

Open AccessArticle

Multi-Stage Stochastic MILP Framework for Renewable Microgrid Dispatch Under High Renewable Penetration: Optimizing Variability and Uncertainty Management

by Olubayo Babatunde, Kunle Fasesin, Adebayo Dosa, Desmond Ighravwe, John Ogbemhe and Oludolapo Olanrewaju

Appl. Sci. 2025, 15(19), 10303; https://doi.org/10.3390/app151910303 - 23 Sep 2025

Viewed by 1572

Abstract

The research develops a multi-stage stochastic Mixed-Integer Linear Programming (MILP) model for managing dispatch schedules in microgrids with significant renewable energy integration. The primary objective is to optimize the integration of renewable energy sources with energy storage systems and grid power, concurrently aiming to reduce operational costs and address uncertainties associated with renewable energy resources. The model effectively captures the variability inherent in renewable sources through the use of scenarios and implements a multi-stage MILP formulation that incorporates storage and load constraints. The methodology employs stochastic optimization techniques to regulate fluctuations in renewable generation by analyzing diverse energy availability scenarios. The optimization process is designed to minimize grid power consumption while maximizing the utilization of renewable energy via storage and load constraints that guarantee a balanced energy supply. The model achieves optimal operational costs by producing results that amount to 46,600 USD while successfully controlling renewable energy variability. The research demonstrates two main achievements by integrating high renewable penetration levels and providing valuable insights into how energy storage systems and grid independence lower costs. Full article

(This article belongs to the Special Issue New Trends in Renewable Energy and Power Systems)

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19 pages, 3371 KB

Open AccessArticle

Prediction of Photovoltaic Module Characteristics by Machine Learning for Renewable Energy Applications

by Rafał Porowski, Robert Kowalik, Bartosz Szeląg, Diana Komendołowicz, Anita Białek, Agata Janaszek, Magdalena Piłat-Rożek, Ewa Łazuka and Tomasz Gorzelnik

Appl. Sci. 2025, 15(16), 8868; https://doi.org/10.3390/app15168868 - 11 Aug 2025

Cited by 2 | Viewed by 2125

Abstract

Photovoltaic (PV) modules undergo comprehensive testing to validate their electrical and thermal properties prior to market entry. These evaluations consist of durability and efficiency tests performed under realistic outdoor conditions with natural climatic influences, as well as in controlled laboratory settings. The overall performance of PV cells is affected by several factors, including solar irradiance, operating temperature, installation site parameters, prevailing weather, and shading effects. In the presented study, three distinct PV modules were analyzed using a sophisticated large-scale steady-state solar simulator. The current–voltage (I-V) characteristics of each module were precisely measured and subsequently scrutinized. To augment the analysis, a three-layer artificial neural network, specifically the multilayer perceptron (MLP), was developed. The experimental measurements, along with the outputs derived from the MLP model, served as the foundation for a comprehensive global sensitivity analysis (GSA). The experimental results revealed variances between the manufacturer’s declared values and those recorded during testing. The first module achieved a maximum power point that exceeded the manufacturer’s specification. Conversely, the second and third modules delivered power values corresponding to only 85–87% and 95–98% of their stated capacities, respectively. The global sensitivity analysis further indicated that while certain parameters, such as efficiency and the ratio of V_oc/V, played a dominant role in influencing the power-voltage relationship, another parameter, U, exhibited a comparatively minor effect. These results highlight the significant potential of integrating machine learning techniques into the performance evaluation and predictive analysis of photovoltaic modules. Full article

(This article belongs to the Special Issue New Trends in Renewable Energy and Power Systems)

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28 pages, 2340 KB

Open AccessArticle

Determining the Operating Performance of an Isolated, High-Power, Photovoltaic Pumping System Through Sensor Measurements

by Florin Dragan, Dorin Bordeasu and Ioan Filip

Appl. Sci. 2025, 15(15), 8639; https://doi.org/10.3390/app15158639 - 4 Aug 2025

Cited by 1 | Viewed by 1100

Abstract

Modernizing irrigation systems (ISs) from traditional gravity methods to sprinkler and drip technologies has significantly improved water use efficiency. However, it has simultaneously increased electricity demand and operational costs. Integrating photovoltaic generators into ISs represents a promising solution, as solar energy availability typically aligns with peak irrigation periods. Despite this potential, photovoltaic pumping systems (PVPSs) often face reliability issues due to fluctuations in solar irradiance, resulting in frequent start/stop cycles and premature equipment wear. The IEC 62253 standard establishes procedures for evaluating PVPS performance but primarily addresses steady-state conditions, neglecting transient regimes. As the main contribution, the current paper proposes a non-intrusive, high-resolution monitoring system and a methodology to assess the performance of an isolated, high-power PVPS, considering also transient regimes. The system records critical electrical, hydraulic and environmental parameters every second, enabling in-depth analysis under various weather conditions. Two performance indicators, pumped volume efficiency and equivalent operating time, were used to evaluate the system’s performance. The results indicate that near-optimal performance is only achievable under clear sky conditions. Under the appearance of clouds, control strategies designed to protect the system reduce overall efficiency. The proposed methodology enables detailed performance diagnostics and supports the development of more robust PVPSs. Full article

(This article belongs to the Special Issue New Trends in Renewable Energy and Power Systems)

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34 pages, 42694 KB

Open AccessArticle

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

Cited by 6 | Viewed by 4617

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 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 KB

Open AccessArticle

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

Cited by 2 | Viewed by 2917

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. 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 KB

Open AccessArticle

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

Cited by 7 | Viewed by 6314

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 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 KB

Open AccessArticle

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

Cited by 9 | Viewed by 2320

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 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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Review

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34 pages, 10897 KB

Open AccessReview

Advances, Progress, and Future Directions of Renewable Wind Energy in Brazil (2000–2025–2050)

by Carlos Cacciuttolo, Martin Navarrete and Deyvis Cano

Appl. Sci. 2025, 15(10), 5646; https://doi.org/10.3390/app15105646 - 19 May 2025

Cited by 7 | Viewed by 7978

Abstract

Brazil has emerged as one of the global leaders in adopting renewable energy, standing out in the implementation of onshore wind energy and, more recently, in the development of future offshore wind energy projects. Onshore wind energy has experienced exponential growth in the last decade, positioning Brazil as one of the countries with the largest installed capacity in the world by 2023, with 30 GW. Wind farms are mainly concentrated in the northeast region, where winds are constant and powerful, enabling efficient and cost-competitive generation. Although in its early stages, offshore wind energy presents significant potential of 1228 GW due to Brazil’s extensive coastline, which exceeds 7000 km. Offshore wind projects promise greater generating capacity and stability, as offshore winds are more constant than onshore winds. However, their development faces challenges such as high initial costs, environmental impacts on marine ecosystems, and the need for specialized infrastructure. From a sustainability perspective, this article discusses that both types of wind energy are key to Brazil’s energy transition. They reduce dependence on fossil fuels, generate green jobs, and foster technological innovation. However, it is crucial to implement policies that foster synergy with green hydrogen production and minimize socio-environmental impacts, such as impacts on local communities and biodiversity. Finally, the article concludes that by 2050, Brazil is expected to consolidate its leadership in renewable energy by integrating advanced technologies, such as larger, more efficient turbines, energy storage systems, and green hydrogen production. The combination of onshore and offshore wind energy and other renewable sources could position the country as a global model for a clean, sustainable, and resilient energy mix. Full article

(This article belongs to the Special Issue New Trends in Renewable Energy and Power Systems)

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