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

Deadline for manuscript submissions: closed (20 November 2025) | Viewed by 17972

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Editors

Dr. Bochao Zhao

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Guest Editor

School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Interests: data analysis for demand-side resouces; virtual power plant
Special Issues, Collections and Topics in MDPI journals

Dr. Bo Liu

E-Mail Website
Guest Editor

School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Interests: smart sensing for smart grids; advanced data analytics for smart grids
Special Issues, Collections and Topics in MDPI journals

Dr. Ying Han

E-Mail Website
Guest Editor

School of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, China
Interests: integrated energy system; transportation electrification
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) technologies are revolutionizing smart grids. They are being extensively explored for their potential to transform the way that we generate, distribute, and consume electricity. This Special Issue will bring together cutting-edge research and innovative applications of AI for the smart grid. It is expected to highlight the significant potential of data analysis, pattern recognition, predictive modelling, etc. Meanwhile, various powerful AI applications in smart grids raise concerns and challenges, such as interpretability, collapse, data dependency, etc. This Special Issue will explore the latest advancements in AI applications for smart grids. We are particularly interested in submissions that focus on the following areas:

AI applications in smart sensing;
AI applications in fault location and protection;
AI applications in power markets and trading;
AI applications in demand-side management;
AI applications in integrated energy systems;
AI applications in transportation electrification;
AI applications in secure energy storage systems.

We also encourage submissions in other, related areas.

Dr. Bochao Zhao
Dr. Bo Liu
Dr. Ying Han
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
smart grid
smart sensing
fault location and protection
power market
demand-side management
integrated energy system
transportation electrification

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Related Special Issue

AI Applications for Smart Grid: 2nd Edition in Electronics (10 articles)

Published Papers (10 papers)

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Research

16 pages, 949 KB

Open AccessArticle

Power Field Hazard Identification Based on Chain-of-Thought and Self-Verification

by Bo Gao, Xvwei Xia, Shuang Zhang, Xingtao Bai, Yongliang Li, Qiushi Cui and Wenni Kang

Electronics 2026, 15(3), 556; https://doi.org/10.3390/electronics15030556 - 28 Jan 2026

Viewed by 524

Abstract

The complex environment of electrical work sites presents hazards that are diverse in form, easily concealed, and difficult to distinguish from their surroundings. Due to poor model generalization, most traditional visual recognition methods are prone to errors and cannot meet the current safety management needs in electrical work. This paper presents a novel framework for hazard identification that integrates chain-of-thought reasoning and self-verification mechanisms within a visual-language large model (VLLM) to enhance accuracy. First, typical hazard scenario data for crane operation and escalator work areas were collected. The Janus-Pro VLLM model was selected as the base model for hazard identification. Then, designing a chain-of-thought enhanced the model’s capacity to identify critical information, including the status of crane stabilizers and the zones where personnel are located. Simultaneously, a self-verification module was designed. It leveraged the multimodal comprehension capabilities of the VLLM to self-check the identification results, outputting confidence scores and justifications to mitigate model hallucination. The experimental results show that integrating the self-verification method significantly improves hazard identification accuracy, with average increases of 2.55% in crane operations and 4.35% in escalator scenarios. Compared with YOLOv8s and D-FINE, the proposed framework achieves higher accuracy, reaching up to 96.3% in crane personnel intrusion detection, and a recall of 95.6%. It outperforms small models by 8.1–13.8% in key metrics without relying on massive labeled data, providing crucial technical support for power operation hazard identification. Full article

(This article belongs to the Special Issue AI Applications for Smart Grid)

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17 pages, 3127 KB

Open AccessArticle

Performance Enhancement of Non-Intrusive Load Monitoring Based on Adaptive Multi-Scale Attention Integration Module

by Guobing Pan, Tao Tian, Haipeng Wang, Zheyu Hu and Beining Lao

Electronics 2026, 15(3), 517; https://doi.org/10.3390/electronics15030517 - 25 Jan 2026

Viewed by 645

Abstract

Non-Intrusive Load Monitoring is an effective method for disaggregating the power consumption of individual appliances from the aggregate load data of a building. The advent of smart meters, Internet of Things devices, and artificial intelligence technologies has significantly advanced the capabilities of non-intrusive load monitoring. However, challenges such as varying sampling frequencies and measurement sensitivities remain. This paper introduces an innovative model incorporating an Adaptive Multi-Scale Attention Integration Module (AMSAIM) to address these issues. The model leverages deep learning and attention mechanisms to improve the accuracy and real-time performance of non-intrusive load monitoring. Validated on the standard UK-DALE dataset, the model consistently demonstrated superior performance. In seen scenarios, our model achieved average F1-scores approximating 0.94 and notably reduced Mean Absolute Error (MAE) values. For washing machines, it achieved an F1-score of 0.99 and MAE of 41.64, outperforming the next best method’s F1-score by 1 percentage point. In challenging unseen scenarios, the model showcased strong generalization, achieving an F1-score of 0.91 for washing machines and reducing MAE to 7.66. Furthermore, an ablation study rigorously confirmed the necessity of the AMSAIM module, showing that the synergistic integration of the efficient multi-scale attention (EMA) and the selective kernel (SK) adaptive receptive field unit is crucial for enhancing model robustness and generalization. Our results highlight the model’s potential for enhancing energy efficiency and providing actionable insights for energy management across various conditions. Full article

(This article belongs to the Special Issue AI Applications for Smart Grid)

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20 pages, 3869 KB

Open AccessArticle

Dynamical Graph Neural Networks for Modern Power Grid Analysis

by Shu Huang, Jining Li, Ruijiang Zeng, Zhiyong Li and Jin Xu

Electronics 2026, 15(3), 493; https://doi.org/10.3390/electronics15030493 - 23 Jan 2026

Cited by 4 | Viewed by 1463

Abstract

Modern power grids are crucial infrastructures underpinning societal stability, yet their complexity and dynamic nature pose significant challenges for traditional analytical methods. Graph Neural Networks (GNNs) have recently emerged as powerful tools for modeling complex relationships in graph-structured data, making them especially suitable for analyzing power systems. However, existing GNN methods typically focus on static or simplified network models, failing to adequately address dynamic topological changes and suffering from the over-smoothing issue. To overcome these limitations, we propose a novel GNN framework incorporating dynamic message-passing mechanisms, comprising Dynamic Topological Learning (DTL) and Adaptive Message-Passing (AMP) modules. Specifically, DTL captures dynamic changes in the power grid topology conditioned on the current state of the system, while AMP dynamically adjusts the message-passing process to effectively preserve local node information according to the updated topology. This framework is model-agnostic, allowing it to be integrated with various GNN architectures. Extensive experiments on multiple benchmark power grid datasets demonstrate that our proposed framework significantly enhances existing GNN methods in power flow and optimal power flow analysis, consistently achieving lower mean absolute error and higher R-squared scores. Full article

(This article belongs to the Special Issue AI Applications for Smart Grid)

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26 pages, 8467 KB

Open AccessArticle

Low-Light Pose-Action Collaborative Network for Industrial Monitoring in Power Systems

by Qifeng Luo, Heng Zhou, Mianting Wu and Qiang Zhou

Electronics 2026, 15(1), 199; https://doi.org/10.3390/electronics15010199 - 1 Jan 2026

Cited by 1 | Viewed by 937

Abstract

Recognizing human actions in low-light industrial environments remains a significant challenge for safety-critical applications in power systems. In this paper, we propose a Low-Light Pose-Action Collaborative Network (LPAC-Net), an integrated framework specifically designed for monitoring scenarios in underground electrical vaults and smart power stations. The pipeline begins with a modified Zero-DCE++ module for reference-free illumination correction, followed by pose extraction using YOLO-Pose and a novel rotation-invariant encoding of keypoints optimized for confined industrial spaces. Temporal dependencies are captured through a bidirectional LSTM network with attention mechanisms to model complex operational behaviors. We evaluate LPAC-Net on the newly curated ARID-Fall dataset, enhanced with industrial monitoring scenarios representative of electrical infrastructure environments. Experimental results demonstrate that our method outperforms state-of-the-art models, including DarkLight-R101, DTCM, FRAGNet, and URetinex-Net++, achieving 95.53% accuracy in recognizing worker activities and safety-critical events. Additional studies confirm LPAC-Net’s robustness under keypoint noise and motion blur, highlighting its practical value for intelligent monitoring in challenging industrial lighting conditions typical of underground electrical facilities and automated power stations. Full article

(This article belongs to the Special Issue AI Applications for Smart Grid)

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25 pages, 3153 KB

Open AccessArticle

Low-Carbon Economic Dispatch of Integrated Energy Systems with Electric Vehicle Participation

by Jingyao Gu, Wei Huang, Chaohao Yan and Kailun Feng

Electronics 2025, 14(23), 4557; https://doi.org/10.3390/electronics14234557 - 21 Nov 2025

Cited by 3 | Viewed by 1057

Abstract

To achieve the coordinated optimization of economic and low-carbon objectives in integrated energy systems, this study develops a synergistic scheduling model combining electric vehicle clusters (V2G) with Power-to-Gas and Carbon Capture and Storage (P2G–CCS) technologies. The system integrates renewable generation (wind and solar) with conventional units, forming an integrated pathway for carbon capture and utilization through the P2G–CCS process. A virtual battery model is adopted to aggregate electric vehicles, whose flexibility is characterized by frequency regulation capacity constraints. Both battery degradation cost and V2G revenue are incorporated into a unified framework to assess the economic feasibility of EV participation. To address the stochastic and volatile nature of renewable generation, typical scenarios are generated through Monte Carlo sampling and scenario reduction for scheduling optimization. Case study results reveal that EVs achieve peak shaving and valley filling through off-peak charging and peak discharging, reducing the total system cost by 5.2%, with V2G revenue offsetting nearly 91% of degradation cost. The coordinated P2G–CCS operation shows remarkable carbon reduction potential, decreasing carbon trading and sequestration costs by approximately 46%. Overall, the proposed model effectively enhances both the economic and environmental performance of the integrated energy system, providing practical guidance for its low-carbon optimal operation. Full article

(This article belongs to the Special Issue AI Applications for Smart Grid)

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26 pages, 11507 KB

Open AccessArticle

PLD-DETR: A Method for Defect Inspection of Power Transmission Lines

by Jianing Chen, Xin Zhang, Dawei Feng, Jiahao Li and Liang Zhu

Electronics 2025, 14(20), 4107; https://doi.org/10.3390/electronics14204107 - 20 Oct 2025

Cited by 5 | Viewed by 1765

Abstract

Unmanned Aerial Vehicle (UAV)-based computer vision has emerged as a crucial approach for transmission line defect detection. However, transmission lines contain multi-scale components in complex environments, thereby complicating the accurate extraction of multi-scale features and necessitating a careful balance between model complexity with detection accuracy. This paper proposes a Transformer-based framework called Power Line Defect Detection Transformer (PLD-DETR). To simultaneously capture shallow texture and deep semantic information while avoiding single-path limitations, a dual-domain selection mechanism block is designed as the backbone network, enabling collaborative feature extraction at different levels. Subsequently, an adaptive sparse self-attention mechanism is introduced to dynamically adjust attention weights for improved processing of critical feature regions, aiming to enhance attention to semantically rich regions and reduce background interference. Finally, we construct a multi-branch auxiliary bidirectional feature pyramid network to address information loss in traditional feature fusion. It fuses multi-scale features from four backbone layers through top-down and bottom-up bidirectional information flow, significantly improving feature representation capability. While maintaining model lightness, experimental results demonstrate that PLD-DETR achieves 2.7%, 7.01%, and 5.58% improvements in AP₅₀, AP₇₅, and AP_50–95, respectively, compared to the baseline model. Compared with other transmission line defect detection methods, PLD-DETR demonstrates superior performance in both accuracy and efficiency Full article

(This article belongs to the Special Issue AI Applications for Smart Grid)

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19 pages, 2604 KB

Open AccessArticle

Bayesian-Optimized GCN-BiLSTM-Adaboost Model for Power-Load Forecasting

by Jiarui Li, Jian Li, Jiatong Li and Guozheng Zhang

Electronics 2025, 14(16), 3332; https://doi.org/10.3390/electronics14163332 - 21 Aug 2025

Cited by 6 | Viewed by 1317

Abstract

Accurate and stable power-load forecasting is crucial for optimizing generation scheduling and ensuring the economic and secure operation of power grids. To address the issues of low prediction accuracy and poor robustness during abrupt load changes, this study proposes a Bayesian-optimized GCN-BiLSTM-Adaboost model (abbreviated as GCN-BiLSTM-AB). It combines Graph Convolutional Networks (GCN), Bidirectional Long Short-Term Memory Networks (BiLSTM), and a Bayesian-optimized AdaBoost framework. Firstly, the GCN is employed to capture the spatial correlation features of the input data. Then, the BiLSTM is employed to extract the long-term dependencies of the data time series. Finally, the AdaBoost framework is used to dynamically adjust the base learner weights, and a Bayesian method is employed to optimize the weight adjustment process and prevent overfitting. The experiment results on actual load data from a regional power grid show the GCN-BiLSTM-AB outperforms other compared models in prediction error metrics, with

M A E

M A P E

, and

R M S E

values of 1.86, 3.13%, and 2.26, respectively, which improve the prediction robustness during load change periods. Therefore, the proposed method shows that the synergistic effect of spatiotemporal feature extraction and dynamic weight adjustment improves prediction accuracy and robustness, which provides a new forecasting model with high precision and reliability for power system dispatch decisions. Full article

(This article belongs to the Special Issue AI Applications for Smart Grid)

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16 pages, 1145 KB

Open AccessArticle

A Hybrid Transformer–Mamba Model for Multivariate Metro Energy Consumption Forecasting

by Liheng Long, Zhiyao Chen, Junqian Wu, Qing Fu, Zirui Zhang, Fan Feng and Ronghui Zhang

Electronics 2025, 14(15), 2986; https://doi.org/10.3390/electronics14152986 - 26 Jul 2025

Cited by 1 | Viewed by 2934

Abstract

With the rapid growth of urban populations and the expansion of metro networks, accurate energy consumption prediction has become a critical task for optimizing metro operations and supporting low-carbon city development. Traditional statistical and machine learning methods often struggle to model the complex, nonlinear, and time-varying nature of metro energy data. To address these challenges, this paper proposes MTMM, a novel hybrid model that integrates the multi-head attention mechanism of the Transformer with the efficient, state-space-based Mamba architecture. The Transformer effectively captures long-range temporal dependencies, while Mamba enhances inference speed and reduces complexity. Additionally, the model incorporates multivariate energy features, leveraging the correlations among different energy consumption types to improve predictive performance. Experimental results on real-world data from the Guangzhou Metro demonstrate that MTMM significantly outperforms existing methods in terms of both MAE and MSE. The model also shows strong generalization ability across different prediction lengths and time step configurations, offering a promising solution for intelligent energy management in metro systems. Full article

(This article belongs to the Special Issue AI Applications for Smart Grid)

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40 pages, 3694 KB

Open AccessArticle

AI-Enhanced MPPT Control for Grid-Connected Photovoltaic Systems Using ANFIS-PSO Optimization

by Mahmood Yaseen Mohammed Aldulaimi and Mesut Çevik

Electronics 2025, 14(13), 2649; https://doi.org/10.3390/electronics14132649 - 30 Jun 2025

Cited by 32 | Viewed by 4514

Abstract

This paper presents an adaptive Maximum Power Point Tracking (MPPT) strategy for grid-connected photovoltaic (PV) systems that uses an Adaptive Neuro-Fuzzy Inference System (ANFIS) optimized by Particle Swarm Optimization (PSO) to enhance energy extraction efficiency under diverse environmental conditions. The proposed ANFIS-PSO-based MPPT controller performs dynamic adjustment Pulse Width Modulation (PWM) switching to minimize Total Harmonic Distortion (THD); this will ensure rapid convergence to the maximum power point (MPP). Unlike conventional Perturb and Observe (P&O) and Incremental Conductance (INC) methods, which struggle with tracking delays and local maxima in partial shading scenarios, the proposed approach efficiently identifies the Global Maximum Power Point (GMPP), improving energy harvesting capabilities. Simulation results in MATLAB/Simulink R2023a demonstrate that under stable irradiance conditions (1000 W/m², 25 °C), the controller was able to achieve an MPPT efficiency of 99.2%, with THD reduced to 2.1%, ensuring grid compliance with IEEE 519 standards. In dynamic irradiance conditions, where sunlight varies linearly between 200 W/m² and 1000 W/m², the controller maintains an MPPT efficiency of 98.7%, with a response time of less than 200 ms, outperforming traditional MPPT algorithms. In the partial shading case, the proposed method effectively avoids local power maxima and successfully tracks the Global Maximum Power Point (GMPP), resulting in a power output of 138 W. In contrast, conventional techniques such as P&O and INC typically fail to escape local maxima under similar conditions, leading to significantly lower power output, often falling well below the true GMPP. This performance disparity underscores the superior tracking capability of the proposed ANFIS-PSO approach in complex irradiance scenarios, where traditional algorithms exhibit substantial energy loss due to their limited global search behavior. The novelty of this work lies in the integration of ANFIS with PSO optimization, enabling an intelligent self-adaptive MPPT strategy that enhances both tracking speed and accuracy while maintaining low computational complexity. This hybrid approach ensures real-time adaptation to environmental fluctuations, making it an optimal solution for grid-connected PV systems requiring high power quality and stability. The proposed controller significantly improves energy harvesting efficiency, minimizes grid disturbances, and enhances overall system robustness, demonstrating its potential for next-generation smart PV systems. Full article

(This article belongs to the Special Issue AI Applications for Smart Grid)

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25 pages, 4826 KB

Open AccessArticle

Optimizing Photovoltaic System Diagnostics: Integrating Machine Learning and DBFLA for Advanced Fault Detection and Classification

by Omar Alqaraghuli and Abdullahi Ibrahim

Electronics 2025, 14(8), 1495; https://doi.org/10.3390/electronics14081495 - 8 Apr 2025

Cited by 7 | Viewed by 1604

Abstract

The rapid growth in photovoltaic (PV) power plant installations has rendered traditional inspection methods inefficient, necessitating advanced approaches for fault detection and classification. This study introduces a novel hybrid metaheuristic method, the Dung Beetle Optimization Algorithm combined with Fick’s Law of Diffusion Algorithm (DBFLA), to address these challenges. The DBFLA enhances the performance of machine learning models, including artificial neural networks (ANNs), support vector machines (SVMs), and ensemble methods, by fine-tuning their parameters to improve fault detection rates. It effectively identifies critical faults such as module mismatches, open circuits, and short circuits. The research demonstrates that DBFLA significantly improves the performance of conventional machine learning techniques by forming a stacking classifier, achieving an individual meta-learner accuracy of approximately 98.75% on real PV datasets. This approach not only accommodates new operating modes and an expanded range of fault conditions but also enhances the reliability of fault detection schemes. The primary contribution of DBFLA lies in its ability to balance exploration and exploitation efficiently, resulting in superior classification accuracy compared to existing optimization techniques. By combining real and simulated datasets, the proposed hybrid method showcases its potential to substantially improve the precision and speed of PV fault detection models. Future work will focus on integrating these advanced models into real-time PV monitoring systems, aiming to reduce detection times and further enhance the reliability and operational efficiency of PV systems. Full article

(This article belongs to the Special Issue AI Applications for Smart Grid)

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