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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 875

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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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

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 202
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 314
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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