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Artificial Intelligence Applications in Electrical and Energy Systems
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Artificial Intelligence Applications in Electrical and Energy Systems

A Special Issue of Electronics (ISSN 2079-9292) belonging to the section "Power Electronics".

Deadline for manuscript submissions: closed (15 November 2025) | Viewed by 3184

Editors

Dr. Noman Shabbir

E-Mail Website
Guest Editor
Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 19086 Tallinn, Estonia
Interests: energy managemnt; machine learning; deep learning; optimization techniques
Special Issues, Collections and Topics in MDPI journals
Dr. Hadi A. Raja

E-Mail Website
Guest Editor
Department of Electrical Power Engineering & Mechatronics, Tallinn University of Technology, 19086 Tallinn, Estonia
Interests: electrical machine; Internet of Things; artificial intelligence; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Special Issue on "Artificial Intelligence Application in Electrical and Energy Systems" aims to explore the transformative impact of AI technologies on the modern electrical and energy sectors. As these fields confront unprecedented challenges related to efficiency, reliability, and sustainability, AI offers innovative solutions for optimizing operations, predicting failures, and integrating renewable energy sources. This Issue will present cutting-edge research and case studies demonstrating how AI techniques such as machine learning, deep learning, and data analytics are being applied to enhance grid management, improve energy storage systems, and develop smart grid technologies. By bridging the gap between AI advancements and practical energy applications, this Special Issue seeks to highlight the potential for AI to drive significant improvements in energy system performance and resilience.

This collection of papers will cover a wide array of topics including, but not limited to, AI-driven predictive maintenance, demand forecasting, energy consumption optimization, and the role of AI in facilitating the transition to decentralized and renewable energy systems. Contributions from both academia and industry are welcomed, providing a comprehensive view of current trends, challenges, and future directions. The Special Issue aims to serve as a valuable resource for researchers, practitioners, and policymakers, fostering a deeper understanding of how AI can be harnessed to address some of the most pressing issues in the electrical and energy sectors. Through this collaborative effort, we hope to inspire the further innovation and adoption of AI technologies to create more efficient, reliable, and sustainable energy systems for the future.

Suggested themes include, but are not limited to, the following:

  • Forecasting applications in power systems;
  • AI-driven predictive maintenance;
  • Demand side flexibility;
  • Energy management;
  • Increasing renewable penetration;
  • AI driven application in power electronics.

In this Special Issue, original research articles and reviews are welcome.

We look forward to receiving your contributions.

Dr. Noman Shabbir
Dr. Hadi A. Raja
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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

  • artificial intelligence
  • electrical machine
  • renewable energy systems

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Published Papers (2 papers)

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Research

24 pages, 3021 KB  
Article
Real-Time Adaptive Optimization for Underwater Optical Wireless Communications Using LSTM–NSGA-II
by Oliger Veronica Mendoza Betancourt and Jianping Wang
Electronics 2026, 15(3), 611; https://doi.org/10.3390/electronics15030611 - 30 Jan 2026
Viewed by 982
Abstract
Underwater optical wireless communication (UOWC) systems are significantly challenged by turbulence-induced signal degradation in dynamic channel conditions. This paper presents a novel framework that integrates Long Short-Term Memory (LSTM) networks with the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to enable real-time turbulence prediction [...] Read more.
Underwater optical wireless communication (UOWC) systems are significantly challenged by turbulence-induced signal degradation in dynamic channel conditions. This paper presents a novel framework that integrates Long Short-Term Memory (LSTM) networks with the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to enable real-time turbulence prediction and multi-objective adaptive optimization of transmission parameters, including power, modulation scheme, and beam divergence. Experimental results demonstrate that the proposed LSTM–NSGA-II framework achieves a 45% reduction in bit error rate (BER) and a 36% improvement in energy efficiency compared to conventional static systems, while maintaining a signal-to-noise ratio (SNR) prediction accuracy of 94.7% and an adaptive response latency of 28.6 ms. Validation using field data from the Marine Institute in the Baltic Sea confirms the framework’s practical applicability and robustness, highlighting its potential to enhance autonomous and military underwater operations in turbulent environments. This work represents a significant step toward more reliable and efficient UOWC systems. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Electrical and Energy Systems)
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16 pages, 1716 KB  
Article
Research on Prediction of Dissolved Gas Concentration in a Transformer Based on Dempster–Shafer Evidence Theory-Optimized Ensemble Learning
by Pan Zhang, Kang Hu, Yuting Yang, Guowei Yi, Xianya Zhang, Runze Peng and Jiaqi Liu
Electronics 2025, 14(7), 1266; https://doi.org/10.3390/electronics14071266 - 24 Mar 2025
Cited by 7 | Viewed by 1360
Abstract
The variation in dissolved gas concentration in the transformer serves as a crucial indicator for assessing the health status and potential faults of the transformer. However, traditional models and existing machine learning and deep learning models exhibit limitations when applied to real-world scenarios [...] Read more.
The variation in dissolved gas concentration in the transformer serves as a crucial indicator for assessing the health status and potential faults of the transformer. However, traditional models and existing machine learning and deep learning models exhibit limitations when applied to real-world scenarios in power systems, lacking adaptability and failing to meet the requirements for accuracy and efficiency of prediction in practical applications. This paper proposes a Dempster–Shafer evidence theory-optimized Bagging ensemble learning model, aiming to improve the accuracy and stability of dissolved gas concentration prediction in transformers. By incorporating Dempster–Shafer evidence theory for the fusion of base learners and optimizing the basic probability distribution parameters by using the sequential least squares programming algorithm, this model significantly improves the adaptability and robustness of prediction. The experimental results show that compared to the ordinary Bagging method and the SARIMA model, the overall mean squared error of the Bagging prediction results optimized by the Dempster–Shafer evidence theory is only 22% of the mean square error of the Bagging prediction results and 38% of the mean square error of the SARIMA prediction results. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Electrical and Energy Systems)
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