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This study presents a data-driven machine learning framework to forecast system inertia service costs, a key but comparatively underexplored factor affecting energy trading and frequency stability in Great Britain. Using eight years of NESO data (2017–2024), four models—LSTM, Residual LSTM, XGBoost, and LightGBM—are compared. Results show LightGBM delivers the highest accuracy, enabling robust inertia cost estimation and actionable market intelligence to support resilient, cost-effective grid operation.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/system-inertia-cost-forecasting-using-machine-learning-a-data-driven-approach-for-grid-energy-trading-in-great-britain/128800/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/system-inertia-cost-forecasting-using-machine-learning-a-data-driven-approach-for-grid-energy-trading-in-great-britain/128800.png","ImageObject",300,407,{"name":92,"@type":93},"Violet","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-18","2026-08-06",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the study address?","Question",{"text":112,"@type":113},"It addresses the need for accurate forecasting of system inertia service costs, which influence energy trading and frequency stability in Great Britain.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which datasets and models are used?",{"text":117,"@type":113},"The study uses eight years of NESO data from 2017 to 2024 and compares four models: LSTM, Residual LSTM, XGBoost, and LightGBM.",{"name":119,"@type":110,"acceptedAnswer":120},"Which model performs best for inertia cost forecasting?",{"text":121,"@type":113},"LightGBM achieves the highest predictive accuracy in the reported results for inertia cost estimation.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},128800,1786003533,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":129,"read_time":144},1099523885336,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","Article  \nSystem Inertia Cost Forecasting Using Machine Learning: A Data-Driven Approach for Grid Energy Trading in Great Britain  \nMaitreyee Dey 1, *, Soumya Prakash Rana 2 and Preeti Patel 1  \nAcademic Editor: Ping-Feng Pai  \nReceived: 4 July 2025  \nRevised: 17 October 2025  \nAccepted: 21 October 2025  \nPublished: 23 October 2025  \nCitation: Dey, M.; Rana, S.P.; Patel, P. System Inertia Cost Forecasting Using Machine Learning: A Data-Driven Approach for Grid Energy Trading in Great Britain. Analytics 2025, 4, 30 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)analytics4040030  \nCopyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) .  \n1 GENESIS Research Lab, Cyber Security Research Centre, London Metropolitan University, 166-220 Holloway Road, London N7 8DB, UK; [p.patel@londonmet.ac.uk](p.patel@londonmet.ac.uk)  \n2 School of Engineering, University of Greenwich, Medway Campus, Central Avenue, Chatham ME4 4TB, UK; [s.rana@greenwich.ac.uk](s.rana@greenwich.ac.uk)  \n* [Correspondence: m.dey@londonmet.ac.uk](Correspondence: m.dey@londonmet.ac.uk)  \nAbstract  \nAs modern power systems integrate more renewable and decentralised generation, maintaining grid stability has become increasingly challenging. This study proposes a datadriven machine learning framework for forecasting system inertia service costs—a key yet underexplored variable influencing energy trading and frequency stability in Great Britain. Using eight years (2017–2024) of National Energy System Operator (NESO) data, four models—Long Short-Term Memory (LSTM), Residual LSTM, eXtreme Gradient Boosting (XGBoost), and Light Gradient-Boosting Machine (LightGBM)—are comparatively analysed. LSTM-based models capture temporal dependencies, while ensemble methods effectively handle nonlinear feature relationships. Results demonstrate that LightGBM achieves the highest predictive accuracy, offering a robust method for inertia cost estimation and market intelligence. The framework contributes to strategic procurement planning and supports market design for a more resilient, cost-effective grid.  \nKeywords: smart grid; frequency; inertia; market estimation; machine learning  \n1. Introduction  \nThe National Energy System Operator (NESO) is responsible for the long-term planning and real-time operation of the electricity and gas systems in Great Britain [1,2] . It ensures a secure and reliable energy supply, promotes efficient energy transmission and distribution, and supports the government’s efforts to achieve its net-zero target. NESO also plays a key role in market operations, system planning, and future-proofing the energy infrastructure.  \nNESO regularly publishes reports—such as system performance reports, balancing services performance monitoring reports, and the GB Electricity System Operator Daily Reports—that provide data on grid performance and operations [3] . In its operability strategy report published in December 2022, National Grid stated that its current policy is to maintain system inertia above 140 GJ. However, by 2025, it aims to maintain a minimum system inertia of 96 GJ [3] . To balance the system, address forecasted energy requirements, and ensure system security, NESO engages in energy trading. This includes trading with third parties to adjust supply and demand and to manage grid constraints.  \nInertia in power systems refers to the energy stored in large rotating generators and certain industrial motors, which enables them to resist changes in rotational speed. This  \nstored energy is especially valuable in the event of a large power plant failure, as it can temporarily compensate for the lost generation [4] . Monitoring system inertia and its","cbCaiavuL5w8xg7M","https://ap.wps.com/l/cbCaiavuL5w8xg7M","pdf",2618340,17,"English","# Introduction\n## System inertia and its costs\n## Role of data and AI/ML\n# Proposed machine learning framework","[{\"question\":\"What problem does the study address?\",\"answer\":\"It addresses the need for accurate forecasting of system inertia service costs, which influence energy trading and frequency stability in Great Britain.\"},{\"question\":\"Which datasets and models are used?\",\"answer\":\"The study uses eight years of NESO data from 2017 to 2024 and compares four models: LSTM, Residual LSTM, XGBoost, and LightGBM.\"},{\"question\":\"Which model performs best for inertia cost forecasting?\",\"answer\":\"LightGBM achieves the highest predictive accuracy in the reported results for inertia cost estimation.\"}]","System Inertia Cost Forecasting Using Machine Learning - A Data-Driven Approach for Grid Energy Trading in Great Britain | PDF",43]