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AI Applications for Smart Grid Energy Management and Industrial Electrical...
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AI Applications for Smart Grid Energy Management and Industrial Electrical Systems

A Special Issue of Computers (ISSN 2073-431X) belonging to the section "AI-Driven Innovations".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 5012

Editors

Dr. Martin Valtierra-Rodriguez

E-Mail Website
Guest Editor
ENAP-RG, CA Sistemas Dinámicos y Control, Departamento de Electromecánica, Facultad de Ingeniería, Universidad Autónoma de Querétaro, Campus San Juan del Río, San Juan del Río 76807, Querétaro, México
Interests: signal processing; machine learning; deep learning; fault diagnosis; electric machines; bio-inspired algorithms; optimization techniques; cyber–physical systems
Special Issues, Collections and Topics in MDPI journals
Dr. David Granados-Lieberman

E-Mail Website
Guest Editor
ENAP-RG-Departamento de Ingeniería Electromecánica, Tecnológico Nacional de México, Instituto Tecnológico Superior de Irapuato, Irapuato 36821, Guanajuato, Mexico
Interests: power quality; electrical control; signal processing; electrical machines; fault diagnosis; smart grids; condition monitoring; transactive energy; renewable energy systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid evolution of electrical systems, from large-scale power grids to renewable energy plants and advanced electrical machines, has created new challenges in monitoring, control, optimization, transactive energy frameworks, and fault diagnosis. These systems are increasingly complex, interconnected, and subject to demanding operational requirements, making intelligent, reliable, and efficient solutions more essential than ever.

Artificial Intelligence (AI) techniques, such as machine learning, deep learning, fuzzy systems, evolutionary computation, and other bio-inspired approaches, have shown remarkable potential to address these challenges. By leveraging powerful computational models and data-driven analysis, AI is transforming how electrical systems are designed, monitored, and maintained, enabling predictive maintenance, enhancing energy efficiency, and improving overall system reliability.

This Special Issue aims to gather state-of-the-art research contributions in the development and application of AI methods for electrical engineering. We encourage works addressing the analysis of electrical signals, fault diagnosis, optimization, and intelligent decision-making in various contexts, including smart grids, renewable energy systems, power quality monitoring and control, transactive energy frameworks, and industrial electrical applications. Contributions combining AI with modern technologies, such as the Internet of Things (IoT), edge computing, digital twins, and embedded systems, are also welcome, as they enable scalable, real-time, and interconnected solutions for electrical systems.

Some research areas may include (but are not limited to) the following:

  • Intelligent algorithms for electrical signal processing and analysis;
  • AI-based monitoring and control of smart grids and renewable energy systems;
  • Power quality assessment and enhancement using computational intelligence;
  • Optimization and decision-making methods for transactive energy frameworks;
  • Fault detection, diagnosis, and prognosis in electrical machines and systems;
  • Applications of deep learning and machine learning in electrical engineering;
  • Integration of AI with IoT, edge computing, and digital twin technologies;
  • Embedded and real-time AI solutions for industrial electrical systems.

We invite original research papers, comprehensive reviews, and case studies that demonstrate novel AI-based approaches for electrical systems.

Dr. Martin Valtierra-Rodriguez
Dr. David Granados-Lieberman
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. Computers is an international peer-reviewed open access monthly 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 1800 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
  • computational intelligence
  • electrical systems
  • fault diagnosis
  • smart grids
  • signal processing
  • power quality
  • electrical control
  • renewable energy systems
  • transactive energy
  • optimization techniques
  • machine learning
  • deep learning
  • bio-inspired computation
  • Internet of Things (IoT)
  • edge computing
  • digital twins
  • embedded systems

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Published Papers (5 papers)

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30 pages, 16681 KB  
Article
FHDG-YOLO: A Frequency-Domain Hybrid Deformation and Geometry-Regression Network for Photovoltaic Crack Detection
by Chenyang Wang, Xinyu Wang, Yilin Wang, Danyu Li, Song Wang and Ying Song
Computers 2026, 15(9), 587; https://doi.org/10.3390/computers15090587 - 5 Sep 2026
Viewed by 179
Abstract
Visible-light photovoltaic (PV) inspection is affected by periodic grid-line backgrounds, low-contrast cracks, irregular crack topology, and the extreme aspect ratios of slender defects. To address these difficulties, a frequency-domain hybrid deformation and geometry-regression YOLO network, termed FHDG-YOLO, is proposed in this study. The [...] Read more.
Visible-light photovoltaic (PV) inspection is affected by periodic grid-line backgrounds, low-contrast cracks, irregular crack topology, and the extreme aspect ratios of slender defects. To address these difficulties, a frequency-domain hybrid deformation and geometry-regression YOLO network, termed FHDG-YOLO, is proposed in this study. The method introduces frequency-domain dynamic decoupled convolution to attenuate periodic background responses, incorporates a high-resolution P2 detection head and efficient multi-scale attention to retain and recalibrate shallow spatial details, embeds DCNv2 to adapt convolutional sampling to irregular defect boundaries, and replaces the original regression loss with MicroShape-IoU for geometry-sensitive localization. Experiments are conducted on a reorganized two-class visible-light PV dataset containing 6493 images, comprising 6262 screened public images and 231 field images collected by the authors. On the 1300-image validation split, FHDG-YOLO obtains a Precision of 0.954, Recall of 0.943, mAP@0.5 of 0.971, and mAP@0.5:0.95 of 0.861. Compared with YOLOv8n, mAP@0.5 and mAP@0.5:0.95 increase by 3.6 and 6.9 percentage points, respectively. On the held-out 649-image test split, the corresponding mAP values are 0.970 and 0.860, compared with 0.931 and 0.785 for YOLOv8n. Under the original four-class Panel Solar validation protocol, FHDG-YOLO obtains mAP@0.5 and mAP@0.5:0.95 values of 0.954 and 0.843, compared with 0.929 and 0.780 for YOLOv8n. Full article
(This article belongs to the Special Issue AI Applications for Smart Grid Energy Management and Industrial Electrical Systems)
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18 pages, 8604 KB  
Article
PEL: An Integrated Algorithm for Power Time Series Anomaly Detection
by Lei Wang, Yu Gao and Xiaoyong Zhao
Computers 2026, 15(6), 396; https://doi.org/10.3390/computers15060396 - 20 Jun 2026
Viewed by 431
Abstract
Power systems continuously generate large-scale load time series data for forecasting, consumption analysis, and equipment health monitoring. However, real-world load measurements are often contaminated by anomalies caused by sensor faults, communication errors, and abnormal consumption behaviors, which may degrade data quality and affect [...] Read more.
Power systems continuously generate large-scale load time series data for forecasting, consumption analysis, and equipment health monitoring. However, real-world load measurements are often contaminated by anomalies caused by sensor faults, communication errors, and abnormal consumption behaviors, which may degrade data quality and affect operational decision-making. To address this issue, this paper proposes an integrated anomaly detection framework named PEL, which combines Prophet-based seasonal-trend decomposition, ensemble empirical mode decomposition (EEMD), and a multilayer long short-term memory (LSTM) network. Prophet is first employed to decompose the original series into trend, seasonal, holiday, and residual components. Sample entropy analysis and white noise tests are then adopted to evaluate whether the residual component still contains complex structured information requiring secondary decomposition. Next, EEMD is applied to the residual component to extract multi-scale intrinsic mode functions. Finally, all decomposed components are normalized and fed into a multilayer LSTM model for anomaly detection. Experiments on a real-world power load dataset demonstrate that the proposed PEL framework achieves an accuracy of 99.92%, a precision of 97.33%, a recall of 100%, an F1-score of 98.65%, and an AUC of 0.9996, outperforming or matching several baseline and hybrid models. Full article
(This article belongs to the Special Issue AI Applications for Smart Grid Energy Management and Industrial Electrical Systems)
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27 pages, 4985 KB  
Article
Hybrid Spatio-Temporal Deep Learning Models for Multi-Task Forecasting in Renewable Energy Systems
by Gulnaz Tolegenova, Alma Zakirova, Maksat Kalimoldayev and Zhanar Akhayeva
Computers 2026, 15(3), 183; https://doi.org/10.3390/computers15030183 - 11 Mar 2026
Cited by 1 | Viewed by 1453
Abstract
Short-term forecasting of solar and wind power generation is critical for smart grid management but challenging due to non-stationarity and extreme generation events. This study addresses a multi-task learning problem: regression-based forecasting of power output and binary detection of extreme events defined by [...] Read more.
Short-term forecasting of solar and wind power generation is critical for smart grid management but challenging due to non-stationarity and extreme generation events. This study addresses a multi-task learning problem: regression-based forecasting of power output and binary detection of extreme events defined by a quantile-based threshold (q = 0.90). A hybrid spatio-temporal model, DP-STH++, is proposed, implementing parallel causal fusion of LSTM, GRU, a causal Conv1D stack, and a lightweight causal transformer. The architecture employs regression and classification heads, while an uncertainty-weighted mechanism stabilizes multitask optimization in the regression tasks; extreme event detection performance is evaluated using AUC. Training and evaluation follow a leakage-safe protocol with chronological data processing, calendar feature integration, time-aware splitting, and training-only estimation of scaling parameters and extreme thresholds. Experimental results obtained with a one-hour forecasting horizon and a 24 h context window demonstrate that DP-STH++ achieves the best regression performance on the hold-out set (RMSE = 257.18, MAE = 174.86–287.90, MASE = 0.2438, R2 = 0.9440) and the highest extreme event detection accuracy (AUC = 0.9896), ranking 1st among all compared architectures. In time-series cross-validation, the model retains the leading position with a mean MASE = 0.3883 and AUC = 0.9709. The advantages are particularly pronounced for wind power forecasting, where DP-STH++ simultaneously minimizes regression errors and maximizes AUC = 0.9880–0.9908. Full article
(This article belongs to the Special Issue AI Applications for Smart Grid Energy Management and Industrial Electrical Systems)
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18 pages, 1381 KB  
Article
Energy-Efficient Container Scheduling Based on Deep Reinforcement Learning in Data Centers
by Zhuohui Li, Shaofeng Zhang, Yiqian Li, Xingchen Liu, Junyang Huang and Jinlong Hu
Computers 2025, 14(12), 560; https://doi.org/10.3390/computers14120560 - 17 Dec 2025
Cited by 3 | Viewed by 1622
Abstract
As data centers become essential large-scale infrastructures for data processing and intelligent computing, the efficiency of their internal scheduling systems is critical for both service quality and energy consumption. The performance of these scheduling systems significantly impacts the quality of computing services and [...] Read more.
As data centers become essential large-scale infrastructures for data processing and intelligent computing, the efficiency of their internal scheduling systems is critical for both service quality and energy consumption. The performance of these scheduling systems significantly impacts the quality of computing services and overall energy usage. However, the rapid increase in task volume, coupled with the diversity of computing resources, poses substantial challenges to traditional scheduling approaches. Conventional container scheduling approaches typically focus on either minimizing task execution time or reducing energy consumption independently, often neglecting the importance of balancing these two objectives simultaneously. In this study, a container scheduling algorithm based on the Soft Actor–Critic framework, called SAC-CS, is proposed. This algorithm aims to enhance container execution efficiency while concurrently reducing energy consumption in data centers. It employs a maximum entropy reinforcement learning approach, enabling a flexible trade-off between energy use and task completion times. Experimental evaluations on both synthetic workloads and Alibaba cluster datasets demonstrate that the SAC-CS algorithm effectively achieves joint optimization of efficiency and energy consumption, outperforming heuristic methods and alternative reinforcement learning techniques. Full article
(This article belongs to the Special Issue AI Applications for Smart Grid Energy Management and Industrial Electrical Systems)
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20 pages, 7625 KB  
Hypothesis
Reducing Peak Load Underprediction Through Risk-Aware Upper Quantile Forecasting: A University Laboratory Case Study
by Marwa O. Al Enany, Mazen Hesham Elnahal and Amira M. Gaber
Computers 2026, 15(8), 524; https://doi.org/10.3390/computers15080524 - 13 Aug 2026
Viewed by 286
Abstract
Underprediction of high electrical demand can be more operationally consequential than an equally sized overprediction, yet standard point forecasting models are optimized primarily for average error. This case study evaluates a multivariate quantile Transformer as a safety-oriented next observation forecasting layer for a [...] Read more.
Underprediction of high electrical demand can be more operationally consequential than an equally sized overprediction, yet standard point forecasting models are optimized primarily for average error. This case study evaluates a multivariate quantile Transformer as a safety-oriented next observation forecasting layer for a university laboratory. A timestamp-level audit identified 33,374 native measurements collected from 22 April to 12 December 2024 at a median interval of approximately 10 min. The final leakage-free pipeline uses only real observations, performs the chronological split before sequence generation, fits all scalers on training data only, and rejects windows containing gaps greater than 30 min. Persistence, fixed-order SARIMA, LSTM, GRU, CNN–LSTM, and an MSE-trained Transformer were evaluated on the same 6595-sample test period. GRU achieved the best deterministic accuracy (MAE 0.017744 kW; RMSE 0.023278 kW), whereas the proposed τ = 0.90 Transformer intentionally traded point accuracy (MAE 0.033830 ± 0.001133 kW) for asymmetric risk control. Across five independent runs, it achieved a pinball loss of 0.004453 ± 0.000069 kW, empirical coverage of 87.95 ± 1.01%, and a peak underprediction rate of 26.64 ± 5.32%, compared with 72.94–100% for the conventional benchmark outputs. Additional τ = 0.75 and τ = 0.95 experiments demonstrate the expected accuracy–safety trade-off. MAPE is not used as a primary metric because near-zero loads make percentage errors unstable. The results support the proposed model as a complementary upper quantile forecasting layer for this small, dynamic facility; they do not establish general performance at feeder or system scale. Full article
(This article belongs to the Special Issue AI Applications for Smart Grid Energy Management and Industrial Electrical Systems)
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