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EDITORIAL article

Front. Energy Res.
Sec. Smart Grids
Volume 12 - 2024 | doi: 10.3389/fenrg.2024.1536459
This article is part of the Research Topic Data-Driven Approaches for Efficient Smart Grid Systems View all 14 articles

Editorial: Data-Driven Approaches for Efficient Smart Grid Systems

Provisionally accepted
Jinran Wu Jinran Wu 1*Yang Yang Yang Yang 2Shaolong Sun Shaolong Sun 3Yang Yu Yang Yu 2
  • 1 Australian Catholic University, Sydney, Australia
  • 2 Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu Province, China
  • 3 Xi'an Jiaotong University, Xi'an, China

The final, formatted version of the article will be published soon.

    Smart grid systems (SGSs) are leading the modernization of energy infrastructure by integrating advanced 10 technologies to improve efficiency, reliability, and sustainability. These systems demand sophisticated tools 11 to address their complexity, with forecasting and optimization being crucial areas of focus. Machine learning 12 (ML) techniques, including both traditional neural networks and advanced deep learning approaches, play 13 a significant role in tackling the intricate challenges of SGSs. These methods enable accurate forecasting, 14 which is essential for predicting electricity demand, renewable energy generation, and system loads. By 15 supporting informed decision-making and efficient resource allocation, ML provides both theoretical 16 contributions and practical applications for smart grids. 17 This special issue, Data-Driven Approaches for Efficient Smart Grid Systems, explores the innovative use 18 of machine learning to address challenges specific to SGSs. Forecasting is central to these efforts, as it forms 19 the basis for understanding and managing the complex dynamics of SGSs. While traditional methods have 20 demonstrated promise, they also highlight limitations in adaptability, scalability, and precision, particularly 21 when addressing the evolving needs of modern smart grids. These challenges call for advanced algorithms 22 that integrate diverse data sources, capture spatiotemporal relationships, and account for uncertainties. The special issue is organized into four thematic areas ("forecasting and prediction techniques", "optimization and scheduling in power systems", "data quality, validation, and identification", and "research 25 trends and evaluations in energy systems"), which highlight the variety of approaches and contributions 26 from the 13 papers accepted. The first area focuses on forecasting and prediction techniques, essential for managing renewable energy

    Keywords: Statistical Modeling, deep learning, Computational Intelligence, Metaheuristics Algorithm, Optimizations

    Received: 28 Nov 2024; Accepted: 13 Dec 2024.

    Copyright: © 2024 Wu, Yang, Sun and Yu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Jinran Wu, Australian Catholic University, Sydney, Australia

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.