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The Implementation of Artificial Intelligence in Energy Internet and Smart Grid Digitization

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: 10 February 2026 | Viewed by 2807

Special Issue Editors

Dr. Yingshu Liu

E-Mail Website
Guest Editor
School of Electrical and Information Engineering, Tianjin University, Tianjin 300387, China
Interests: optimal operation scheduling of the electric power system; smart monitoring and control systems for microgrids and virtual power plants; electric market for distributed generations and energy storage units
Dr. Xi Chen

E-Mail Website
Guest Editor
School of Electrical and Information Engineering, Tianjin University, Tianjin 300387, China
Interests: artificial intelligence technologies and their implementations in electric automation systems
Dr. Haibo Pen

E-Mail Website
Guest Editor
The School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China
Interests: data mining and data analysis; application of AI/ML in smart grid; cyber physical power system

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is the key impetus for the advancement of Energy Internet and Smart Grid Digitization; it has been widely implemented in numerous areas, such as the intelligent prediction of load profiles and renewable energy generation, optimal system planning and operation scheduling, fault detection and diagnostics, digital twins, big models for electric power systems, etc. This Special Issue aims to present and disseminate state-of-the-art theories, designs, modelling, and applications of these technologies in the scope of Energy Internet and Smart Grid Digitization.

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

  • Intelligent prediction of loads and renewable energies;
  • Optimal planning of electric power systems, distributed renewable generation, and energy storage units;
  • Optimal operation control for power systems, virtual power plants, and the demand side;
  • The implementation of artificial intelligence in integrated energy systems;
  • The implementation of artificial intelligence in energy marketing;
  • Digital twins and big models for electric power systems.

Dr. Yingshu Liu
Dr. Xi Chen
Dr. Haibo Pen
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. 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)
  • energy internet
  • smart grid digitization
  • integrated energy system
  • intelligent prediction
  • optimal scheduling
  • energy market
  • digital twins
  • big model

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Published Papers (3 papers)

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Research

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17 pages, 3413 KB  
Article
A Parameter-Free Fault Location Algorithm for Hybrid Transmission Lines Using Double-Ended Data Synchronization and Physics-Informed Neural Networks
by Guangjie Yang, Guojun Xu, Ruijing Jiang, Yanfeng Jiang, Xiaolong Chen, Lirong Sun, Yitong Li and Yihan Gao
Energies 2025, 18(21), 5710; https://doi.org/10.3390/en18215710 - 30 Oct 2025
Cited by 1 | Viewed by 472
Abstract
Accurate fault location is crucial for enabling maintenance personnel to quickly reach the fault site for inspection and repair, thereby minimizing power outage duration. To address the low fault location accuracy caused by phase unsynchronization of double-ended recording data and the dependence of [...] Read more.
Accurate fault location is crucial for enabling maintenance personnel to quickly reach the fault site for inspection and repair, thereby minimizing power outage duration. To address the low fault location accuracy caused by phase unsynchronization of double-ended recording data and the dependence of traditional algorithms on accurate line parameters, this paper introduces a novel fault location algorithm for hybrid transmission lines. The method integrates a data synchronization approach with a physics-informed neural network (PINN) implemented using a backpropagation (BP) neural network architecture. First, the proposed synchronization algorithm corrects the phase misalignment between double-ended recordings. Second, a distributed-parameter fault location model is developed to derive a location function, which is then used to construct physics-informed input features. This approach reduces the need for large fault datasets, addressing the challenge of the low occurrence of faults in practice. Finally, a BP neural network employing these physics-informed features is utilized to learn the nonlinear mapping to the fault location, allowing for accurate fault location, enabling accurate positioning without requiring precise line parameters. Validation using actual line data confirms the high precision of the synchronization algorithm. Furthermore, simulations show that the proposed fault location algorithm achieves high accuracy and remains robust against variations in fault position, type, transition resistance, inception angle, and load current, making it highly practical for real engineering applications. Full article
(This article belongs to the Special Issue The Implementation of Artificial Intelligence in Energy Internet and Smart Grid Digitization)
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23 pages, 5040 KB  
Article
Intelligent Modelling Techniques for Enhanced Thermal Comfort and Energy Optimisation in Residential Buildings
by Shamaila Iram, Hafiz Muhammad Athar Farid, Abduljelil Adeola Akande and Hafiz Muhammad Shakeel
Energies 2025, 18(14), 3878; https://doi.org/10.3390/en18143878 - 21 Jul 2025
Cited by 2 | Viewed by 938
Abstract
This study examines the utilisation of sophisticated predictive methodologies to enhance the energy efficiency and comfort of residential structures. The ASHRAE Global Thermal Comfort Database II was employed to construct and evaluate machine learning models that were designed to predict thermal comfort levels [...] Read more.
This study examines the utilisation of sophisticated predictive methodologies to enhance the energy efficiency and comfort of residential structures. The ASHRAE Global Thermal Comfort Database II was employed to construct and evaluate machine learning models that were designed to predict thermal comfort levels while optimising energy consumption. Air temperature, garment insulation, metabolic rate, air velocity, and humidity were identified as critical comfort determinants. Numerous predictive models were assessed, and XGBoost demonstrated improved performance as a result of hyperparameter optimisation (R2 = 0.9394, MSE = 0.0224). The study underscores the ability of sophisticated algorithms to clarify the complex relationships between environmental factors and occupant comfort. This sophisticated modelling methodology provides a practical approach to enhancing the efficiency of residential energy consumption while simultaneously ensuring the comfort of the occupants, thereby promoting more sustainable and comfortable living environments. Full article
(This article belongs to the Special Issue The Implementation of Artificial Intelligence in Energy Internet and Smart Grid Digitization)
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Review

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26 pages, 320 KB  
Review
Generative AI for Sustainable Smart Environments: A Review of Energy Systems, Buildings, and User-Centric Decision-Making
by Dimitrios Vamvakas, Ioannis Papaioannou, Christos Tsaknakis, Thomas Sgouros and Christos Korkas
Energies 2025, 18(23), 6163; https://doi.org/10.3390/en18236163 - 24 Nov 2025
Viewed by 934
Abstract
The rapid evolution of Generative Artificial Intelligence (GenAI) is reshaping the energy sector, enabling new levels of adaptability, efficiency, and user-centric interaction. This review systematically maps and critically evaluates the chosen literature across buildings, grids, and urban systems. Through major scientific databases and [...] Read more.
The rapid evolution of Generative Artificial Intelligence (GenAI) is reshaping the energy sector, enabling new levels of adaptability, efficiency, and user-centric interaction. This review systematically maps and critically evaluates the chosen literature across buildings, grids, and urban systems. Through major scientific databases and for the span of five years, from 2021 to 2025, the review aims to identify key application domains, synergies, and research gaps. The analysis on recent advancements illustrates how GenAI enhances energy forecasting, demand–response strategies, anomaly detection, and cyber-resilience in power networks, while also supporting predictive modeling and optimal control in distributed renewable integration. Within smart buildings, GenAI empowers autonomous agents and AI copilots to balance comfort with energy efficiency through adaptive environmental control and user preference modeling. At the grid level, generative models improve renewable generation forecasting, grid stability, and decision support for operators. A further emerging application lies in the generation of synthetic energy data, which supports model training, scenario simulation, and robust decision-making in data-scarce environments. In the broader context of smart cities, GenAI-driven digital twins, multi-agent systems, and conversational interfaces facilitate sustainable planning and energy-aware citizen engagement. A central theme across these applications is the alignment of technological solutions with human needs and sustainability objectives. Key challenges remain in uncertainty quantification, trustworthy deployment, and data governance, underscoring the need for secure, adaptive, and human-centered GenAI systems to drive the next generation of intelligent energy management. This review provides a comprehensive analysis to promote a better understanding of generative models as they are being applied in a variety of scenarios in the energy domain. Full article
(This article belongs to the Special Issue The Implementation of Artificial Intelligence in Energy Internet and Smart Grid Digitization)
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