Prediction of the Demand Response Behavior of Residential Consumers through a Bayesian Forecasting Method

Prediction of the Demand Response Behavior of Residential Consumers through a Bayesian Forecasting Method | 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 Prediction of the Demand Response Behavior of Residential Consumers through a Bayesian Forecasting Method Zohreh Kaheh, Hamidreza Arasteh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2557715/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 Demand-side resources have been recognized as a cost-effective approach for providing the required flexibility in power systems with high penetrations of renewable resources. Almost all existing demand response (DR) models are established based on the assumption that end-users are always rational and predictable agents with well-defined preferences. This assumption has resulted in apparently unaccountable divergence between modelled and observed results for DR approaches. However, due to the nature of the prompt responses of DR providers to DR signals, it is of paramount importance to consider the effect of impulsive decisions on providing the flexibility. This paper investigates the effects of impulsive decisions on the frequency of being unresponsive to DR signals and the collaboration rate in 5-minutes ahead DRX through a Bayesian Forecasting Method. The analyses have demonstrated considering the provided flexibility in similar days of week as explanatory variable in Bayesian structural time series can be helpful for the detection of impulsive behavior to attain more accurate DR behavior forecasting. Behavior Analysis Demand Response Flexibility Markov Chain Model Bayesian Forecasting Method Full Text Additional Declarations No competing interests reported. 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. 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-2557715","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":174000547,"identity":"e6b242c6-278f-4252-9f32-7b5de4a1a089","order_by":0,"name":"Zohreh Kaheh","email":"","orcid":"","institution":"Niroo Research Institute (NRI)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zohreh","middleName":"","lastName":"Kaheh","suffix":""},{"id":174000551,"identity":"d70a8e85-bccd-4df1-99e2-885b7ba7db6e","order_by":1,"name":"Hamidreza Arasteh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYBACAyidwA8kDoBYbMRpSWBIkGwgWYvBAWIdZs5+9uHHnz9s8oxv9z48wFBjx8AnTUCzZU+6sTRPQlqx2Z3jQIuOJTOw8SUQcNiBNAZphoTDidtupAH9wgZEPAQcZnD+GfPPHwn/EzfPAGn5R4yWG2lsEjwJBxI3SAC1MLYRpeUZmzVPWnLiDJDDEvuSeYhwWBrzzR82don9M9KYP3z4Zicn30NACypIYGAgZMcoGAWjYBSMAmIAAO3kPyRs1Af5AAAAAElFTkSuQmCC","orcid":"","institution":"Niroo Research Institute (NRI)","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Hamidreza","middleName":"","lastName":"Arasteh","suffix":""}],"badges":[],"createdAt":"2023-02-06 22:44:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2557715/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2557715/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49247960,"identity":"98fd2df7-fbf7-4d65-8005-b3f0385a3466","added_by":"auto","created_at":"2024-01-05 22:52:54","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":350996,"visible":true,"origin":"","legend":"","description":"","filename":"Paper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2557715/v1_covered_6740bba1-a1dc-4fc4-aad1-11b82d17a8f4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prediction of the Demand Response Behavior of Residential Consumers through a Bayesian Forecasting Method","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Behavior Analysis, Demand Response, Flexibility, Markov Chain Model, Bayesian Forecasting Method","lastPublishedDoi":"10.21203/rs.3.rs-2557715/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2557715/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDemand-side resources have been recognized as a cost-effective approach for providing the required flexibility in power systems with high penetrations of renewable resources. 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