A probabilistic forecasting framework for neighbourhood-level disaggregation of electric vehicle adoption scenarios

A probabilistic forecasting framework for neighbourhood-level disaggregation of electric vehicle adoption scenarios | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A probabilistic forecasting framework for neighbourhood-level disaggregation of electric vehicle adoption scenarios Isaac Flower, Furong Li, Julian Padget This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7074730/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract The rapid growth of electric vehicle (EV) adoption presents significant challenges for electricity networks, particularly at the low-voltage level, where clustered neighbourhood demand risks overloading infrastructure. Existing scenario-based planning approaches typically assume uniform EV uptake across neighbourhoods within a region, failing to capture the heterogeneity evident in historical EV registration data. They provide limited quantification of uncertainty, despite the difficulty of predicting future adoption at fine spatial scales. This paper introduces a Gaussian process (GP)-based forecasting framework that combines granular historical EV registration data with top-down regional scenarios to generate probabilistic neighbourhood-level forecasts. The GP captures how local adoption deviates from regional trends, encoded in the GP’s mean function, ensuring consistency with broader scenarios while accounting for local variation and uncertainty. We validate the framework using ten representative local authority districts in England and Wales, covering a total of 1,294 neighbourhoods. The framework outperforms baseline methods (scaled scenario, logistic growth, linear extrapolation) in normalised mean absolute error, with statistically significant improvements at horizons of three years and beyond. It also delivers well-calibrated prediction intervals, providing reliable uncertainty estimates. This framework offers a practical tool for network operators, policymakers, and planners to support targeted decision-making and investment. Physical sciences/Engineering/Energy infrastructure/Energy grids and networks Physical sciences/Engineering/Electrical and electronic engineering Physical sciences/Energy science and technology/Energy infrastructure/Energy grids and networks Scientific community and society/Energy and society/Energy supply and demand Full Text Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7074730","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":485598405,"identity":"145ab649-9648-4647-8b63-3841dfae90c7","order_by":0,"name":"Isaac Flower","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYBACNiD+8MHAJoGBgQciIkGEFsaZMyrSEhjYiNUCBIyzec4cJkELn0T6wwbetvN5/PN7DzD8qGFInNlAyGESCYkNkm23iyWO8SUw9hxjSJxNyBagluMPDNtuJzYc4zFg4G1gSJxHWEtiY0Ni27nE+UAtjH+J05LM2HDgzIHEDUAtzCBbCDuM5xljY0NFcrHhsRyDwzLHJIwJel++Pf1h8x8Duzy5w2cMH76psZGdcYCQNQIJCPYB4iKSn6Cho2AUjIJRMOIBAOqpQanLh7XWAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0008-3111-7558","institution":"University of Bath","correspondingAuthor":true,"prefix":"","firstName":"Isaac","middleName":"","lastName":"Flower","suffix":""},{"id":485598406,"identity":"b5769638-9715-4d69-8405-a820edf82f7d","order_by":1,"name":"Furong Li","email":"","orcid":"","institution":"University of Bath","correspondingAuthor":false,"prefix":"","firstName":"Furong","middleName":"","lastName":"Li","suffix":""},{"id":485598407,"identity":"57df6dfb-1bc4-4ac1-b905-4fb60b95e3d9","order_by":2,"name":"Julian Padget","email":"","orcid":"","institution":"University of Bath","correspondingAuthor":false,"prefix":"","firstName":"Julian","middleName":"","lastName":"Padget","suffix":""}],"badges":[],"createdAt":"2025-07-08 12:10:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7074730/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7074730/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87884868,"identity":"39d9adc6-f540-4237-a570-502eda63d9d6","added_by":"auto","created_at":"2025-07-30 05:01:53","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4551133,"visible":true,"origin":"","legend":"Article File","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7074730/v1_covered_295f616f-584e-49e6-91a9-91535eb27b5d.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"A probabilistic forecasting framework for neighbourhood-level disaggregation of electric vehicle adoption scenarios","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7074730/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7074730/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The rapid growth of electric vehicle (EV) adoption presents significant challenges for electricity networks, particularly at the low-voltage level, where clustered neighbourhood demand risks overloading infrastructure. Existing scenario-based planning approaches typically assume uniform EV uptake across neighbourhoods within a region, failing to capture the heterogeneity evident in historical EV registration data. They provide limited quantification of uncertainty, despite the difficulty of predicting future adoption at fine spatial scales. This paper introduces a Gaussian process (GP)-based forecasting framework that combines granular historical EV registration data with top-down regional scenarios to generate probabilistic neighbourhood-level forecasts. The GP captures how local adoption deviates from regional trends, encoded in the GP’s mean function, ensuring consistency with broader scenarios while accounting for local variation and uncertainty. We validate the framework using ten representative local authority districts in England and Wales, covering a total of 1,294 neighbourhoods. The framework outperforms baseline methods (scaled scenario, logistic growth, linear extrapolation) in normalised mean absolute error, with statistically significant improvements at horizons of three years and beyond. It also delivers well-calibrated prediction intervals, providing reliable uncertainty estimates. 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