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Intelligent Optimization and Machine Learning in Power and Energy Systems

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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Power Electronics".

Deadline for manuscript submissions: 15 May 2026 | Viewed by 15779

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Special Issue Editors

Dr. Yiannis Katsigiannis

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Guest Editor

Department of Electrical and Computer Engineering, Hellenic Mediterranean University, GR-71004 Heraklion, Greece
Interests: power generation; power systems; wind energy; energy efficiency; power production; renewable energy; solar cells; mechanical engineering; solar energy
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Dr. Konstantinos Blazakis

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Guest Editor

School of Electrical and Computer Engineering, Technical University of Crete, GR-73100 Chania, Greece
Interests: machine learning; data mining; RES power output forecasting; power theft detection; smart grids; distributed generation; electric vehicles

Special Issue Information

Dear Colleagues,

Power and energy systems are critical infrastructures that require efficient, reliable, and sustainable actions. The complexity of these systems is increasing continuously due to the integration of a number of factors, including renewable energy sources, energy storage, electric vehicles, smart grids, and demand-side management. As a result, traditional methods for optimization and control are often inadequate in these systems. Intelligent optimization and machine learning offer promising solutions to address these challenges by transforming the way power and energy systems are designed, operated, and maintained. This Special Issue is devoted to addressing these issues by presenting recent and novel methodologies that are related to intelligent optimization and machine learning in power and energy systems.

Dr. Yiannis Katsigiannis
Dr. Konstantinos Blazakis
Guest Editors

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Keywords

optimization
machine learning and data mining
artificial intelligence
power systems
smart grids
energy storage
renewable energy integration
renewable energy forecasting
load forecasting and demand-side management
grid reliability and resilience

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Published Papers (9 papers)

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Research

Jump to: Review

13 pages, 2221 KB

Open AccessArticle

Solar-Tracker Diffuse-Response Algorithm for Balancing Energy Gain and Mechanical Wear in Photovoltaic Systems

by Riccardo Adinolfi Borea, Silvana Ovaitt, Vincenzo Cirimele, Mattia Ricco and Giosuè Maugeri

Electronics 2026, 15(3), 597; https://doi.org/10.3390/electronics15030597 - 29 Jan 2026

Viewed by 350

Abstract

Single-axis solar tracking maximizes photovoltaic energy production under clear-sky conditions; however, its effectiveness decreases under cloudy and overcast skies, where diffuse irradiance dominates and the optimal module orientation changes. Conventional tracking algorithms either neglect sky conditions or rely on simplified diffuse-response strategies that may trigger frequent tracker repositioning under variable cloud cover, leading to increased mechanical wear with marginal energy gains. This work proposes an enhanced diffuse-response tracking algorithm that explicitly accounts for both the intensity and temporal persistence of cloudiness. By requiring overcast conditions to persist for a minimum duration before reorienting the tracker to a diffuse-stow position, the proposed approach reduces unnecessary movements while preserving the benefits of diffuse-response operation. The algorithm is evaluated through numerical simulations based on historical meteorological data and validated using field measurements on monofacial and bifacial photovoltaic strings. The results show that the proposed strategy reduces excess tracker movement from 114% to 0.16% while maintaining nearly the same energy yield. Compared to a conventional diffuse-response algorithm, the associated energy reduction is minimal (≈0.17%) relative to the ≈0.37% yield gain observed at the studied location. These findings demonstrate that incorporating cloudiness duration enables a practical compromise between energy performance and tracker durability, particularly for monofacial photovoltaic systems. Full article

(This article belongs to the Special Issue Intelligent Optimization and Machine Learning in Power and Energy Systems)

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17 pages, 8374 KB

Open AccessArticle

ML-Based Multi-Horizon Wind Speed and Wind Direction Forecasting for Aviation and Energy Applications in Coastal Crete

by Konstantinos Blazakis, Stelios Perrakis, Emmanuel Karapidakis, Fotis Mavromatakis and Yiannis Katsigiannis

Electronics 2025, 14(24), 4856; https://doi.org/10.3390/electronics14244856 - 10 Dec 2025

Viewed by 637

Abstract

Accurate short-term and medium-term wind speed and wind direction forecasting are crucial for aviation safety, renewable energy management, and environmental planning, particularly in coastal areas with complex terrains. In this study, four machine learning models (Temporal Fusion Transformer (TFT), LightGBM, CatBoost, and a multi-head Convolutional Neural Network (CNN)) were used for multi-horizon (1–24 h) forecasting in Kastelli, Crete, near the new Heraklion International Airport, using a high-resolution multivariate meteorological dataset (2015–2023). For the wind speed forecasting, the best mean absolute error (MAE) values at each horizon are 1 h = 1.89 m/s (LightGBM), 6 h = 3.12 m/s (CatBoost), 12 h = 3.44 m/s (TFT), and 24 h = 3.38 m/s (TFT). For the wind direction forecasting, the best angular MAE values are 1 h = 8.66° (CatBoost), 6 h = 30.71° (CNN), 12 h = 35.29° (TFT), and 24 h = 25.65° (TFT). Overall, the study indicates that different models outperform at different horizons, with the tree-based models being the most effective for short-term forecasts, the convolutional network performing best at intermediate horizons, and the transformer-based architecture offering stronger performance over longer horizons. Compared to recent literature, the proposed framework achieves measurable improvements and confirms the feasibility of deploying ML-based forecasting tools. Full article

(This article belongs to the Special Issue Intelligent Optimization and Machine Learning in Power and Energy Systems)

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22 pages, 4001 KB

Open AccessArticle

SolPowNet: Dust Detection on Photovoltaic Panels Using Convolutional Neural Networks

by Ömer Faruk Alçin, Muzaffer Aslan and Ali Ari

Electronics 2025, 14(21), 4230; https://doi.org/10.3390/electronics14214230 - 29 Oct 2025

Cited by 2 | Viewed by 1234

Abstract

In recent years, the widespread adoption of photovoltaic (PV) panels for electricity generation has provided significant momentum toward sustainable energy goals. However, it has been observed that the accumulation of dust and contaminants on panel surfaces markedly reduces efficiency by blocking solar radiation from reaching the surface. Consequently, dust detection has become a critical area of research into the energy efficiency of PV systems. This study proposes SolPowNet, a novel Convolutional Neural Network (CNN) model based on deep learning with a lightweight architecture that is capable of reliably distinguishing between images of clean and dusty panels. The performance of the proposed model was evaluated by testing it on a dataset containing images of 502 clean panels and 340 dusty panels and comprehensively comparing it with state-of-the-art CNN-based approaches. The experimental results demonstrate that SolPowNet achieves an accuracy of 98.82%, providing 5.88%, 3.57%, 4.7%, 18.82%, and 0.02% higher accuracy than the AlexNet, VGG16, VGG19, ResNet50, and Inception V3 models, respectively. These experimental results reveal that the proposed architecture exhibits more effective classification performance than other CNN models. In conclusion, SolPowNet, with its low computational cost and lightweight structure, enables integration into embedded and real-time applications. Thus, it offers a practical solution for optimizing maintenance planning in photovoltaic systems, managing panel cleaning intervals based on data, and minimizing energy production losses. Full article

(This article belongs to the Special Issue Intelligent Optimization and Machine Learning in Power and Energy Systems)

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14 pages, 9483 KB

Open AccessArticle

Optimizing an Urban Water Infrastructure Through a Smart Water Network Management System

by Evangelos Ntousakis, Konstantinos Loukakis, Evgenia Petrou, Dimitris Ipsakis and Spiros Papaefthimiou

Electronics 2025, 14(12), 2455; https://doi.org/10.3390/electronics14122455 - 17 Jun 2025

Cited by 5 | Viewed by 2728

Abstract

Water, an essential asset for life and growth, is under growing pressure due to climate change, overpopulation, pollution, and industrialization. At the same time, water distribution within cities relies on piping networks that are over 30 years old and thereby prone to leaks, cracking, and losses. Taking this into account, non-revenue water (i.e., water that is distributed to homes and facilities but not returning revenues) is estimated at almost 50%. To this end, intelligent water management via computational advanced tools is required in order to optimize water usage, to mitigate losses, and, more importantly, to ensure sustainability. To address this issue, a case study was developed in this paper, following a step-by-step methodology for the city of Heraklion, Greece, in order to introduce an intelligent water management system that integrates advanced technologies into the aging water distribution infrastructure. The first step involved the digitalization of the network’s spatial data using geographic information systems (GIS), aiming at enhancing the accuracy and accessibility of water asset mapping. This methodology allowed for the creation of a framework that formed a “digital twin”, facilitating real-time analysis and effective water management. Digital twins were developed upon real-time data, validated models, or a combination of the above in order to accurately capture, simulate, and predict the operation of the real system/process, such as water distribution networks. The next step involved the incorporation of a hydraulic simulation and modeling tool that was able to analyze and calculate accurate water flow parameters (e.g., velocity, flowrate), pressure distributions, and potential inefficiencies within the network (e.g., loss of mass balance in/out of the district metered areas). This combination provided a comprehensive overview of the water system’s functionality, fostering decision-making and operational adjustments. Lastly, automatic meter reading (AMR) devices could then provide real-time data on water consumption and pressure throughout the network. These smart water meters enabled continuous monitoring and recording of anomaly detections and allowed for enhanced control over water distribution. All of the above were implemented and depicted in a web-based environment that allows users to detect water meters, check water consumption within specific time-periods, and perform real-time simulations of the implemented water network. Full article

(This article belongs to the Special Issue Intelligent Optimization and Machine Learning in Power and Energy Systems)

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22 pages, 1657 KB

Open AccessArticle

Wind Power Prediction Using a Dynamic Neuro-Fuzzy Model

by George Kandilogiannakis, Paris Mastorocostas, Athanasios Voulodimos, Constantinos Hilas and Dimitrios Varsamis

Electronics 2025, 14(12), 2326; https://doi.org/10.3390/electronics14122326 - 6 Jun 2025

Cited by 1 | Viewed by 908

Abstract

A Dynamic Neuro-fuzzy Model (Dynamic Neuro-fuzzy Wind Predictor, DNFWP) is proposed for wind power prediction. The fuzzy rules in DNFWP consist of a typical antecedent part with static inputs, while the consequent part is a small three-layer neural network, incorporating unit feedback connections at the outputs of the neurons of the hidden layer. The inclusion of internal feedback targets to capture the intrinsic temporal relations of the dataset, while maintaining the local modeling approach of traditional fuzzy models. Each rule in DNFWP represents a local model, and the fuzzy rules operate cooperatively through the defuzzification process. The fuzzy rule base is extracted employing the Fuzzy C-means clustering algorithm, and the consequent neural networks’ weights are tuned by the use of Dynamic Resilient Propagation. Two cases with datasets of different volumes are tested and the performance of DNFWP is very promising, according to the results attained using a series of metrics like Root Mean Squared Error, Mean Absolute Error, and the r-squared statistic. The dynamic nature of the predictor allows it to operate effectively with a single input, thus rendering a feature selection phase unnecessary. DNFWP is compared to Machine Learning-based and Deep Learning-based counterparts, such that its prediction capabilities along with its reduced parametric complexity are highlighted. Full article

(This article belongs to the Special Issue Intelligent Optimization and Machine Learning in Power and Energy Systems)

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18 pages, 1621 KB

Open AccessArticle

Performance Optimization of Machine-Learning Algorithms for Fault Detection and Diagnosis in PV Systems

by Eduardo Quiles-Cucarella, Pedro Sánchez-Roca and Ignacio Agustí-Mercader

Electronics 2025, 14(9), 1709; https://doi.org/10.3390/electronics14091709 - 23 Apr 2025

Cited by 12 | Viewed by 3180

Abstract

The early detection of faults in photovoltaic (PV) systems is crucial for ensuring efficiency, minimizing energy losses, and extending operational lifespan. This study evaluates and compares multiple machine-learning models for fault diagnosis in PV systems, analyzing their performance across different fault types and operational modes. A dataset comprising 2.2 million measurements from a laboratory-based PV model, covering seven fault categories—including inverter failures, partial shading, and sensor faults—is used for training and validation. Models are assessed under both Maximum Power Point Tracking (MPPT) and Limited Power Point Tracking (LPPT) conditions to determine their adaptability. The results indicate that the ensemble bagged tree classifier achieves the highest accuracy (92.2%) across all fault scenarios, while neural network-based models perform better under MPPT conditions. Additionally, the study highlights variations in model performance based on power mode, suggesting the potential for adaptive diagnostic approaches. The findings reinforce the feasibility of machine learning for predictive maintenance in PV systems, offering a cost-effective, sensor-free method for real-time fault detection. Future research should explore hybrid models that dynamically switch between classifiers based on system conditions, as well as validation using real-world PV installations. Full article

(This article belongs to the Special Issue Intelligent Optimization and Machine Learning in Power and Energy Systems)

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21 pages, 1158 KB

Open AccessArticle

Analyzing the Effect of Error Estimation on Random Missing Data Patterns in Mid-Term Electrical Forecasting

by Ayaz Hussain, Paolo Giangrande, Giuseppe Franchini, Lorenzo Fenili and Silvio Messi

Electronics 2025, 14(7), 1383; https://doi.org/10.3390/electronics14071383 - 29 Mar 2025

Cited by 6 | Viewed by 1883

Abstract

In smart buildings, time series forecasting of electrical load is essential for energy optimization, demand response, and overall building performance. However, the mid-term load forecasting (MTLF) can be particularly challenging due to several uncertainties, such as sensor malfunctions, communication failures, and external environmental factors. These problems can lead to missing data patterns that may impact the accuracy and reliability of forecasting models. The purpose of this study is to explore the impact of random missing data patterns on the MTLF predictions’ accuracy. Therefore, several data imputation techniques are evaluated using a complete dataset (i.e., with no missing values) acquired on a smart commercial building, and their influence on load forecasting performance is assessed when different percentages of randomly distributed missing data patterns are assumed. Moreover, the deep learning (DL) approach based on a recurrent neural network, namely, long short-term memory (LSTM), is employed to predict the smart building electrical energy consumption. The obtained outcomes demonstrate that the pattern of random missing data significantly impacts the forecasting accuracy, with machine learning (ML) imputation techniques having better results than statistical and hybrid imputation techniques. Based on these findings, it is evident that robust data preprocessing and the handling of missing values are important in order to improve the accuracy and reliability of mid-term electrical load forecasts. Full article

(This article belongs to the Special Issue Intelligent Optimization and Machine Learning in Power and Energy Systems)

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Review

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59 pages, 3596 KB

Open AccessReview

Beginner-Friendly Review of Research on R-Based Energy Forecasting: Insights from Text Mining

by Minjoong Kim, Hyeonwoo Kim and Jihoon Moon

Electronics 2025, 14(17), 3513; https://doi.org/10.3390/electronics14173513 - 2 Sep 2025

Cited by 1 | Viewed by 2092

Abstract

Data-driven forecasting is becoming increasingly central to modern energy management, yet nonspecialists without a background in artificial intelligence (AI) face significant barriers to entry. While Python is the dominant machine learning language, R remains a practical and accessible tool for users with expertise in statistics, engineering, or domain-specific analysis. To inform tool selection, we first provide an evidence-based comparison of R with major alternatives before reviewing 49 peer-reviewed articles published between 2020 and 2025 in Science Citation Index Expanded (SCIE)-level journals that utilized R for energy forecasting tasks, including electricity (regional and site-level), solar, wind, thermal energy, and natural gas. Despite such growth, the field still lacks a systematic, cross-domain synthesis that clarifies which R-based methods prevail, how accessible workflows are implemented, and where methodological gaps remain; this motivated our use of text mining. Text mining techniques were employed to categorize the literature according to forecasting objectives, modeling methods, application domains, and tool usage patterns. The results indicate that tree-based ensemble learning models—e.g., random forests, gradient boosting, and hybrid variants—are employed most frequently, particularly for solar and short-term load forecasting. Notably, few studies incorporated automated model selection or explainable AI; however, there is a growing shift toward interpretable and beginner-friendly workflows. This review offers a practical reference for nonexperts seeking to apply R in energy forecasting contexts, emphasizing accessible modeling strategies and reproducible practices. We also curate example R scripts, workflow templates, and a study-level link catalog to support replication. The findings of this review support the broader democratization of energy analytics by identifying trends and methodologies suitable for users without advanced AI training. Finally, we synthesize domain-specific evidence and outline the text-mining pipeline, present visual keyword profiles and comparative performance tables that surface prevailing strategies and unmet needs, and conclude with practical guidance and targeted directions for future research. Full article

(This article belongs to the Special Issue Intelligent Optimization and Machine Learning in Power and Energy Systems)

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21 pages, 1710 KB

Open AccessReview

Application of Deep Learning Algorithms for Scenario Analysis of Renewable Energy-Integrated Power Systems: A Critical Review

by Shima Rahmani, Nima Amjady and Rakibuzzaman Shah

Electronics 2025, 14(11), 2150; https://doi.org/10.3390/electronics14112150 - 25 May 2025

Cited by 7 | Viewed by 1680

Abstract

As the global shift towards renewable energy sources accelerates, the challenge of effectively modeling the inherent uncertainty associated with these energy units becomes increasingly significant. Sustainable energy sources, like solar and wind power sources, are highly variable and difficult to predict, making their integration into power systems complex. Beyond renewable energy, other critical sources of uncertainty also influence power systems’ operations, including fluctuations in electricity prices and variations in load demand. To address these uncertainties, stochastic programming has become a widely adopted approach. Preparation of the required scenarios for a stochastic programming framework typically includes two main components: scenario generation and reduction. Scenario generation involves creating a diverse set of possible future outcomes based on various uncertainties considered, while scenario reduction focuses on refining these scenarios to a manageable number without losing any essential piece of information. In this paper, we explore the innovative methods used for scenario generation and scenario reduction, with a special emphasis on deep learning approaches. Additionally, we provide future research recommendation, identify areas for further development, and discuss the challenges associated with these deep learning methods. Full article

(This article belongs to the Special Issue Intelligent Optimization and Machine Learning in Power and Energy Systems)

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