Forecasting Day-Ahead Electricity Prices in Irish Markets: Time Series and Machine Learning Approaches and Performance Evaluation

Forecasting Day-Ahead Electricity Prices in Irish Markets: Time Series and Machine Learning Approaches and Performance Evaluation | 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 Research Article Forecasting Day-Ahead Electricity Prices in Irish Markets: Time Series and Machine Learning Approaches and Performance Evaluation Purnachandra Mandadapu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4233273/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Electricity price forecasting has become increasingly vital following the liberalization of electricity markets—ushering in a more competitive environment for electricity generation and distribution. Notably, electricity prices in Day-Ahead Markets (DAMs) hold significant sway, influencing decisions made by energy traders. However, modeling electricity prices poses challenges due to their inherent characteristics such as heteroscedasticity, sharp price spikes, and multiple levels of seasonality. Therefore, in this study, we delve into various methodologies from existing literature to forecast electricity spot prices within the Irish DAM. Our focus lies on employing time series and Machine Learning (ML) techniques to predict prices for all 24 hours of each DAM auction facilitated by the Single Electricity Market Operator (SEMO). For this, we begin by providing a concise overview of the electricity market and its functioning, particularly concerning our objective of price forecasting within the DAM. Subsequently, we elucidate the key aspects of the data utilized in this study. Following this, we offer succinct explanations of each model employed, detailing their structures and preparatory steps for the modeling task. Central to our analysis are the results showcasing the performance of each model relative to a benchmark, along with a brief discussion on the significance of predictors in the forecasting process. Finally, based on our findings, we draw conclusions and outline potential avenues for further research and development. Day-Ahead Markets Electricity Machine Learning Single Electricity Market Operator Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted 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. 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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-4233273","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":288575879,"identity":"31a5e77d-cf34-4fc8-847f-14aacb1467fa","order_by":0,"name":"Purnachandra Mandadapu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYLACHgZmIMl8QIKhAMgmQQtbggSDAQMPDxvxWngMQFoYCGoxFzv87MGbGmt5c4mcjzc+GNjI2Ms3H2D4UbENpxbL2WnmhnOOpRvunJG72XKGQRrQYWwJjD1nbuPUYnA7wUyah+0w44YbudukeQwOA7XwGDAztuHTkv5NmuffYfsNN3KeSf8Ba+H/QEBLjpk0b9vhRKAWNmkGiC0MeLVYzs4pk5zbl568s+eZsWUPyC/H0gwO4vOLuXT6Nok336xtt7MnP7zxo8LGnr358MMHPyrwOAyDAQIHcKrHqWUUjIJRMApGATIAAJY5UPOKRNkBAAAAAElFTkSuQmCC","orcid":"","institution":"Deliotte","correspondingAuthor":true,"prefix":"","firstName":"Purnachandra","middleName":"","lastName":"Mandadapu","suffix":""}],"badges":[],"createdAt":"2024-04-08 01:57:52","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-4233273/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4233273/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54340111,"identity":"49e19ded-0e93-4980-bfaf-7e6faa3914a6","added_by":"auto","created_at":"2024-04-09 04:55:05","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":461944,"visible":true,"origin":"","legend":"","description":"","filename":"ForecastingDayAheadElectricityPricesinIrishMarketsTimeSeriesandMachineLearningApproachesandPerformanceEvaluation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4233273/v1_covered_6ab949da-99fb-4b0b-98a9-115efe435f75.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eForecasting Day-Ahead Electricity Prices in Irish Markets: Time Series and Machine Learning Approaches and Performance Evaluation\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Day-Ahead Markets, Electricity, Machine Learning, Single Electricity Market Operator","lastPublishedDoi":"10.21203/rs.3.rs-4233273/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4233273/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eElectricity price forecasting has become increasingly vital following the liberalization of electricity markets\u0026mdash;ushering in a more competitive environment for electricity generation and distribution. 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Following this, we offer succinct explanations of each model employed, detailing their structures and preparatory steps for the modeling task. Central to our analysis are the results showcasing the performance of each model relative to a benchmark, along with a brief discussion on the significance of predictors in the forecasting process. 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