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Electronics
Special Issues
AI Applications for Smart Grid: 2nd Edition
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AI Applications for Smart Grid: 2nd Edition

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 15 August 2026 | Viewed by 353

Special Issue Editors

Dr. Bochao Zhao

E-Mail Website
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
Dr. Zhen Zhao

E-Mail Website
Guest Editor
Shanghai Artificial Intelligence Laboratory, Shanghai, China
Interests: data-efficient deep learning; multimodal perception and reasoning; AI for Energy
Dr. Fei Zhang

E-Mail Website
Guest Editor
School of Automation and Electrical Engineering, Inner Mongolia University of Science and Technology, Baotou 014000, China
Interests: cyber-physical systems; industrial internet data fusion; optimal control of intelligent new energy station systems; automation systems; communication systems; information system scheme design

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) technologies are revolutionizing smart grids, being extensively explored for their potential to transform the way that we generate, distribute, and consume electricity. The objective of this Special Issue is to compile cutting-edge research and innovative applications of AI for the smart grid. It seeks to highlight the significant potential of data analysis, pattern recognition, predictive modelling, etc. Moreover, various powerful AI applications in smart grids present challenges regarding, for example, interpretability, collapse, and data dependency. This Special Issue aims to 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 the submission of papers on other related areas.

Dr. Bochao Zhao
Dr. Bo Liu
Dr. Ying Han
Dr. Zhen Zhao
Dr. Fei Zhang
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-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

  • 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

Published Papers (1 paper)

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Research

16 pages, 1691 KB  
Article
Weakly Supervised Optimization for Power Distribution Transformer Area Identification Based on Frequency-Domain Representation
by Suwei Zhai, Junkai Liang, Wangxia Yang, Chao Zheng, Dongdong Wang, Xiaodong Xing and Yanjun Feng
Electronics 2026, 15(5), 1000; https://doi.org/10.3390/electronics15051000 - 28 Feb 2026
Viewed by 131
Abstract
Accurate identification of user–transformer relationships is fundamental to refined management, load forecasting, and fault diagnosis in low-voltage distribution networks. Traditional approaches often rely on costly manual inspection or complex physical modeling, which limits their scalability. This paper proposes a frequency-domain representation learning and [...] Read more.
Accurate identification of user–transformer relationships is fundamental to refined management, load forecasting, and fault diagnosis in low-voltage distribution networks. Traditional approaches often rely on costly manual inspection or complex physical modeling, which limits their scalability. This paper proposes a frequency-domain representation learning and weakly supervised optimization method for automatic transformer-area identification from large-scale user electricity data with incomplete labels. Specifically, the proposed method first applies the Fast Fourier Transform (FFT) to convert users’ voltage and current time series into robust frequency-domain feature vectors, effectively revealing intrinsic periodic structures while reducing noise interference. Then, under limited supervision, a deep metric learning framework is employed to optimize the embedding space such that users belonging to the same transformer area are clustered more compactly, while those from different areas are separated farther apart. Finally, a high-density clustering algorithm is applied in the optimized embedding space to complete the transformer-area partition for all users. Experimental results demonstrate that the proposed approach can effectively leverage limited label information and significantly improve transformer-area identification accuracy, providing an efficient and low-cost solution for digitalized operation and maintenance of low-voltage distribution networks. Full article
(This article belongs to the Special Issue AI Applications for Smart Grid: 2nd Edition)
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