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AI, Big Data, and IoT for Smart Grids and Electric Vehicles

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".

Deadline for manuscript submissions: 31 July 2026 | Viewed by 3013

Editor

Dr. Joao Ferreira

E-Mail Website
Guest Editor
Department of Information Sciences, Technologies and Architecture, University Institute of Lisbon, 1649-026 Lisbon, Portugal
Interests: electric vehicle; data science; IoT; blockchain; artificial intelligence; smart grids
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The accelerating global transition toward sustainable energy and intelligent transportation systems has brought unprecedented opportunities and challenges. This Special Issue focuses on the convergence of Artificial Intelligence (AI), Big Data Analytics, and the Internet of Things (IoT) in optimizing smart grids and electric vehicle (EV) ecosystems.

We invite original research, review articles, and case studies that address innovative solutions, architectures, frameworks, and policy insights to improve the performance, security, interoperability, and sustainability of smart energy and mobility systems.

Topics of interest include, but are not limited to the following:

  1. AI-driven forecasting and optimization for energy demand and supply;
  2. Big data architectures for real-time smart grid analytics;
  3. IoT-based energy monitoring and load management;
  4. Integration of EVs into smart grid infrastructure (V2G, G2V, and V2X);
  5. Edge and fog computing for distributed energy systems;
  6. Blockchain and digital identity in smart grid and EV ecosystems;
  7. Cybersecurity, data privacy, and trust frameworks;
  8. Predictive maintenance and fault detection in EV charging infrastructure;
  9. Smart metering, grid resilience, and demand response;
  10. AI and IoT for renewable energy integration and energy storage systems;
  11. Federated learning and privacy-preserving analytics in energy systems;
  12. User behavior analytics and energy consumption profiling;
  13. Digital twins for smart grid operations and EV fleet management.

Dr. Joao Ferreira
Guest Editor

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. Energies 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 2600 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 (AI)
  • big data analytics
  • the internet of things (IoT)
  • energy demand
  • smart grid
  • electric vehicle
  • distributed energy systems
  • energy storage systems
  • load management

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Published Papers (3 papers)

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Research

21 pages, 13989 KB  
Article
Android-Based Real-Time Classification of Electric Fire Short-Circuit Traces Using Lightweight Deep Learning Model
by Mohammad Hadi Nazari and Junho Bang
Energies 2026, 19(13), 3184; https://doi.org/10.3390/en19133184 - 4 Jul 2026
Viewed by 182
Abstract
This paper presents a lightweight deep learning framework for classifying electric fire short-circuit traces to enhance safety and fault diagnosis in electrical energy systems. Accurate differentiation between primary (PSCT) and secondary short-circuit traces (SSCT) is essential for identifying failure origins, yet conventional manual [...] Read more.
This paper presents a lightweight deep learning framework for classifying electric fire short-circuit traces to enhance safety and fault diagnosis in electrical energy systems. Accurate differentiation between primary (PSCT) and secondary short-circuit traces (SSCT) is essential for identifying failure origins, yet conventional manual inspection is time-consuming and subjective. To address these limitations, we systematically evaluate three lightweight convolutional neural network (CNN) architectures MobileNetV2, MobileNetV3, and EfficientNet using transfer learning on a domain-specific image dataset. The models are assessed based on accuracy, loss, precision, recall, and F1-score. Experimental results show that EfficientNet achieves the highest classification accuracy, while MobileNetV3 demonstrates the lowest validation loss and superior generalization stability. Based on a performance–efficiency trade-off analysis, MobileNetV3 is deployed on an Android platform using TensorFlow Lite, enabling real-time, offline, and on-device inference. To the best of our knowledge, this is among the first studies to integrate lightweight CNN-based short-circuit trace classification with real-time mobile deployment for on-site energy system fault analysis. By bridging the gap between deep learning and field deployment, the proposed mobile system ensures low-latency execution and provides a rapid, reliable, and portable solution for improving operational safety in electrical fire investigations. Full article
(This article belongs to the Special Issue AI, Big Data, and IoT for Smart Grids and Electric Vehicles)
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36 pages, 3396 KB  
Article
Graph-Enhanced Prompt Tuning for Evidence-Grounded HFACS Classification in Power-System Safety
by Wenhua Zeng, Wenhu Tang, Diping Yuan, Bo Zhang, Na Xu and Hui Zhang
Energies 2025, 18(20), 5389; https://doi.org/10.3390/en18205389 - 13 Oct 2025
Cited by 2 | Viewed by 1183
Abstract
Power-system safety is fundamental to protecting lives and ensuring reliable grid operation. Yet, hierarchical text classification (HTC) methods struggle with domain-dense accident narratives that require cross-sentence reasoning, often yielding limited fine-grained recognition, inconsistent label paths, and weak evidence traceability. We propose EG-HPT (Evidence-Grounded [...] Read more.
Power-system safety is fundamental to protecting lives and ensuring reliable grid operation. Yet, hierarchical text classification (HTC) methods struggle with domain-dense accident narratives that require cross-sentence reasoning, often yielding limited fine-grained recognition, inconsistent label paths, and weak evidence traceability. We propose EG-HPT (Evidence-Grounded Hierarchy-Aware Prompt Tuning), which augments hierarchical prompt tuning with Global Pointer-based nested-entity recognition and a sentence–entity heterogeneous graph to aggregate cross-sentence cues; label-aware attention selects Top-k evidence nodes and a weighted InfoNCE objective aligns label and evidence representations, while a hierarchical separation loss and an ancestor-completeness constraint regularize the taxonomy. On a HFACS-based power-accident corpus, EG-HPT consistently outperforms strong baselines in Micro-F1, Macro-F1, and path-constrained Micro-F1 (C-Micro-F1), with ablations confirming the contributions of entity evidence and graph aggregation. These results indicate a deployable, interpretable solution for automated risk factor analysis, enabling auditable evidence chains and supporting multi-granularity accident intelligence in safety-critical operations. Full article
(This article belongs to the Special Issue AI, Big Data, and IoT for Smart Grids and Electric Vehicles)
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16 pages, 2441 KB  
Article
Federated Hybrid Graph Attention Network with Two-Step Optimization for Electricity Consumption Forecasting
by Hao Yang, Xinwu Ji, Qingchan Liu, Lukun Zeng, Yuan Ai and Hang Dai
Energies 2025, 18(17), 4465; https://doi.org/10.3390/en18174465 - 22 Aug 2025
Cited by 1 | Viewed by 1115
Abstract
Electricity demand forecasting is essential for smart grid management, yet it presents challenges due to the dynamic nature of consumption trends and regional variability in usage patterns. While federated learning (FL) offers a privacy-preserving solution for handling sensitive, region-specific data, traditional FL approaches [...] Read more.
Electricity demand forecasting is essential for smart grid management, yet it presents challenges due to the dynamic nature of consumption trends and regional variability in usage patterns. While federated learning (FL) offers a privacy-preserving solution for handling sensitive, region-specific data, traditional FL approaches struggle when local datasets are limited, often leading models to overfit noisy peak fluctuations. Additionally, many regions exhibit stable, periodic consumption behaviors, further complicating the need for a global model that can effectively capture diverse patterns without overfitting. To address these issues, we propose Federated Hybrid Graph Attention Network with Two-step Optimization for Electricity Consumption Forecasting (FedHMGAT), a hybrid modeling framework designed to balance periodic trends and numerical variations. Specifically, FedHMGAT leverages a numerical structure graph with a Gaussian encoder to model peak fluctuations as dynamic covariance features, mitigating noise-driven overfitting, while a multi-scale attention mechanism captures periodic consumption patterns through hybrid feature representation. These feature components are then fused to produce robust predictions. To enhance global model aggregation, FedHMGAT employs a two-step parameter aggregation strategy: first, a regularization term ensures parameter similarity across local models during training, and second, adaptive dynamic fusion at the server tailors aggregation weights to regional data characteristics, preventing feature dilution. Experimental results verify that FedHMGAT outperforms conventional FL methods, offering a scalable and privacy-aware solution for electricity demand forecasting. Full article
(This article belongs to the Special Issue AI, Big Data, and IoT for Smart Grids and Electric Vehicles)
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