Joint Probabilistic Day-Ahead Energy Forecast for Power System Operations

Joint Probabilistic Day-Ahead Energy Forecast for Power System Operations | 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 Joint Probabilistic Day-Ahead Energy Forecast for Power System Operations Guillermo Terrén-Serrano, Ranjit Deshmukh, Manel Martínez-Ramón This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5891000/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Feb, 2026 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract Increasing shares of wind and solar generation and rising electricity demand introduce uncertainty in power system operations. Improving short-term day-ahead forecasts of renewable energy generation and demand is critical for system operators to reduce the risk of forecasting errors, coordinate operations of the electricity market, and minimize the cost of maintaining the power system reliability. Here, we incorporate the joint probability distribution between electricity demand and energy generated from solar and wind sources to characterize system-level uncertainties in demand and supply. We develop a robust, scalable probabilistic forecasting methodology for generating system-level day-ahead forecasts of electricity demand and wind and solar generation based on publicly available weather forecasts. We combine four Sparse learning methods that identify relevant weather variables with four Bayesian learning methods that quantify the uncertainty in the forecast and evaluate each combination of these methods using proper scoring rules. Applying these models to the three zones of the California Independent System Operator, we find that the best model combination improves the system operator's forecast by 25.2%. Additionally, the confidence intervals from joint electricity demand and generation probabilistic forecast enable more effective allocation of operating reserve levels compared to current deterministic forecasts. Physical sciences/Energy science and technology/Renewable energy Physical sciences/Engineering/Energy infrastructure/Energy grids and networks Physical sciences/Engineering/Electrical and electronic engineering Gaussian Process Load forecast Post-processing Probabilistic Forecast Solar forecast Wind forecast Full Text Additional Declarations There is NO Competing Interest. Supplementary Files jointforecastsupplementarymaterial.pdf Supplementary Information Cite Share Download PDF Status: Published Journal Publication published 28 Feb, 2026 Read the published version in Nature Communications → 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-5891000","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":410912891,"identity":"a48894e8-8ad2-499d-9dac-3c04b61ecb80","order_by":0,"name":"Guillermo Terrén-Serrano","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYFACxsYDD+CcCiBmZm4gpKXhQAIDgwSEcwakhZGQFgYGhBbGNogheJXLRyQDbalgqOOXPvvwceG82mj+dqCWHxXbcGoxvJEI1HKGQUKyL93YeOa247kzDjM2MPacuY1bywyglsQ2BgmDM2xs0rzbjuU2ALUwM7YR0vKPQcL+DBv7b945x3LnE9IiLwHS0gC0hYeNjZm3oSZ3AyEtBjwPgX45JiE54wwbszTPsQO5G4FaDuLzi3x7+sMHH2ps+Pl72Bg/89TU5c47f/jggx8VeGw5AKYkYPzDYPIATvUgWxpQ+XX4FI+CUTAKRsEIBQDoglnRHiPIYQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-8688-2299","institution":"University of California Santa Barbara","correspondingAuthor":true,"prefix":"","firstName":"Guillermo","middleName":"","lastName":"Terrén-Serrano","suffix":""},{"id":410912892,"identity":"5203c85f-8d7f-4b02-ad0a-d82fac5330a2","order_by":1,"name":"Ranjit Deshmukh","email":"","orcid":"https://orcid.org/0000-0002-5593-675X","institution":"University of California, Santa Barbara","correspondingAuthor":false,"prefix":"","firstName":"Ranjit","middleName":"","lastName":"Deshmukh","suffix":""},{"id":410912893,"identity":"4c75c45e-233a-42e7-832b-810c645a7349","order_by":2,"name":"Manel Martínez-Ramón","email":"","orcid":"","institution":"University of New Mexico","correspondingAuthor":false,"prefix":"","firstName":"Manel","middleName":"","lastName":"Martínez-Ramón","suffix":""}],"badges":[],"createdAt":"2025-01-23 21:05:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5891000/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5891000/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41467-026-69015-w","type":"published","date":"2026-02-28T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":106583871,"identity":"84d43a22-cb04-47ac-9963-091330528ce6","added_by":"auto","created_at":"2026-04-10 07:13:52","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7050839,"visible":true,"origin":"","legend":"Article File","description":"","filename":"jointforecastmanuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5891000/v1_covered_c0d9a3c7-4114-4a4a-97da-d629de08655a.pdf"},{"id":80686143,"identity":"843af6fd-6f06-4239-a77e-bca121f04cfc","added_by":"auto","created_at":"2025-04-16 03:42:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":4578897,"visible":true,"origin":"","legend":"Supplementary Information","description":"","filename":"jointforecastsupplementarymaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5891000/v1/08c043d2a776c939063629d1.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Joint Probabilistic Day-Ahead Energy Forecast for Power System Operations","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"Gaussian Process, Load forecast, Post-processing, Probabilistic Forecast, Solar forecast, Wind forecast","lastPublishedDoi":"10.21203/rs.3.rs-5891000/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5891000/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Increasing shares of wind and solar generation and rising electricity demand introduce uncertainty in power system operations. 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