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Electronics
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Intelligent Optimization and Machine Learning in Power and Energy Systems
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Intelligent Optimization and Machine Learning in Power and Energy Systems

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

Deadline for manuscript submissions: 15 October 2025 | Viewed by 1817

Special Issue Editors

Dr. Yiannis Katsigiannis

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals
Dr. Konstantinos Blazakis

E-Mail Website
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

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. Electronics 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

  • 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 (4 papers)

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Research

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22 pages, 1657 KiB  
Article
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
Viewed by 165
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 [...] Read more.
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 KiB  
Article
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
Viewed by 448
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 [...] Read more.
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 KiB  
Article
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
Viewed by 472
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 [...] Read more.
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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21 pages, 1710 KiB  
Review
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
Viewed by 263
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 [...] Read more.
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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