[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126384-en":3,"doc-seo-126384-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126384,962085564549,"Genevieve","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Machine learning in peak demand forecasting: foundations, trends, and insights","Peak demand forecasting focuses on predicting maximum electricity demand within a defined period, supporting power-system efficiency and stability. Expanding advanced metering, localized energy applications such as electric vehicles, and the growing share of intermittent renewables increase randomness and weaken predictability. A comprehensive review of 186 studies since the 1950s proposes three development stages and a unified forecasting framework, connecting methods to real power-system needs. It highlights the rising value of machine learning models, key challenges, and future research trends.","Renewable and Sustainable Energy Reviews 227 (2026) 116500  \nContents lists available at ScienceDirect  \nRenewable and Sustainable Energy Reviews  \njournal [homepage:](homepage: www. elsevier. com/locate/rser)[ www. elsevier. com/locate/rser](homepage: www. elsevier. com/locate/rser)  \n| Machine learning in peak demand forecasting: foundations, trends, and insights\u003Cbr>Shuang Daia,* iD, Fanlin Mengb iD, Hongsheng Daic, Qian Wangd iD, Xizhong Chen e iD, Wenlei BaifiD, Peizhi Shig, Richard Allmendingerh iD, Yuchen Zhangi, j, Jian Liuka Department of Engineering, University of Exeter, Exeter, UK\u003Cbr>b University of Exeter Business School, University of Exeter, Exeter, UK\u003Cbr>c School of Mathematics, Statistics and Physics, Newcastle University, Newcastle, UK d Department of Computer Science, Durham University, Durham, UK\u003Cbr>e School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai, China f Electric Reliability Council of Texas (ERCOT), Taylor, Tx 76574, USA\u003Cbr>g Centre for Decision Research, Leeds University Business School, University of Leeds, Leeds, UK h Alliance Manchester Business School, University of Manchester, Manchester, UK\u003Cbr>i Institute for Analytics and Data Science, University of Essex, Colchester, UK\u003Cbr>j School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK\u003Cbr>k Kummer Institute Center for Artificial Intelligence and Autonomous Systems, Missouri University of Science and Technology, Rolla, USA |  |  |\n| --- | --- | --- |\n| H I G H L I G H T S |  |  |\n| • First comprehensive review of peak demand forecasting, analyzing 186 studies.\u003Cbr>• Categorized studies into three stages and defined a unified forecasting framework.\u003Cbr>• Analyzed the evolving role of machine learning in different application contexts.\u003Cbr>• Identified key challenges and outlined emerging trends for future research. |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>Deep learning\u003Cbr>Machine learning\u003Cbr>Peak demand forecasting Power system\u003Cbr>Smart grid\u003Cbr>Time-series analysis |  | Peak demand forecasting involves predicting the maximum electricity demand within a specific period, which plays a key role in maintaining the efficiency and stability of power systems. The rapid evolution of power systems, driven by advanced metering infrastructure, local energy applications such as electric vehicles, and the increasing adoption of intermittent renewable energy, has introduced greater randomness and reduced predictability in peak demand. Given the pressing need to address more diverse implementation requirements across different contexts, accurate and reliable peak demand forecasting has become increasingly important. To the best of our knowledge, this study is the first to provide a comprehensive overview of peak demand forecasting methods. It systematically reviews 186 studies published since the 1950s, categorizing these methods into three stages based on their developmental timeline. Building on this, the study defines a unified framework for peak demand forecasting and offers an in-depth analysis linking these methods to the practical needs of power systems. Notably, it highlights the growing importance of machine learning-driven forecasting models in addressing the increasing complexity of modern energy environments. Furthermore, this study identifies key research gaps and points out emerging trends that hold potential for advancing innovation in this field. |\n\n* Corresponding author.  \nEmail address: [s.dai@exeter.ac.uk](s.dai@exeter.ac.uk) (S. Dai).  \n[https://doi.org/10.1016/j.rser.2025.116500](https://doi.org/10.1016/j.rser.2025.116500)  \nReceived 6 January 2025; Received in revised form 19 October 2025; Accepted 14 November 2025  \nAvailable online 26 November 2025  \n1364-0321/© 2025 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ([http://creativecommons.org/licenses/by](http://creativecommons.org/licenses/b","cbCaia5eDzNOHnOC","https://ap.wps.com/l/cbCaia5eDzNOHnOC","pdf",5739299,10,1,37,"English","en",105,"# Highlights\n## Comprehensive review and unified framework\n## Machine learning role and emerging trends\n# Article Abstract\n## Forecasting problem and motivations\n## Review scope and three-stage categorization\n## Unified forecasting framework and research gaps\n# Keywords","[{\"question\":\"What problem does peak demand forecasting solve in power systems?\",\"answer\":\"It predicts the maximum electricity demand within a specific period, helping maintain the efficiency and stability of power systems.\"},{\"question\":\"Why has peak demand become harder to predict?\",\"answer\":\"Power-system evolution—advanced metering, electric vehicles and other local applications, and higher intermittent renewable penetration—introduces more randomness and reduces predictability.\"},{\"question\":\"What is the main contribution of the reviewed research in this study?\",\"answer\":\"It provides a comprehensive overview of methods by reviewing 186 studies, organizing them into three stages, and defining a unified forecasting framework linked to practical power-system needs.\"}]","Machine learning in peak demand forecasting: foundations, trends, and insights | 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