Enhanced RBAθ method for uncertainty quantification in time varying dataset

Enhanced RBAθ method for uncertainty quantification in time varying dataset | 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 Enhanced RBA θ method for uncertainty quantification in time varying dataset Purbak Sengupta, Sambeet Mishra This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7810964/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 The high velocity and variability of wind power data introduce challenges in reliably detecting ramp events and quantifying uncertainty. To address this, we propose the enhanced Ramping Behavior Analysis (RBAθ ) framework, which extends the original RBAθ method by introducing two adaptive thresholding strategies: a statistical inference-based threshold and a Random Forest–based Markov Chain Monte Carlo (RF–MCMC) threshold. These replace static thresholds with data-driven, uncertainty-aware mechanisms. Empirical evaluation on wind capacity factor datasets shows that the enhanced RBAθ -Traditional (statistical thresholding) achieves an overall performance score of 0.82, with perfect robustness (1.00) and strong balance (0.94). Compared to existing approaches, it improves consistency by 20.7% over Sliding Window Ramp Threshold (SWRT) and balance by 88.8% over Cumulative Sum (CUSUM), while enabling reliable detection of both significant and stationary ramp events. Comparative analysis further indicates that while adaptive SWRT attains the highest overall score (0.86), the enhanced RBAθ -Traditional offers greater robustness and stability, making it a more reliable solution for wind ramp event detection. Time-series variation uncertainty quantification wind power adaptive thresholding ramping behavior analysis machine learning Random Forest Bayesian inference event detection renewable energy systems 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-7810964","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":526716063,"identity":"1e8c927b-4f42-4dbd-94c3-c1e686a79382","order_by":0,"name":"Purbak Sengupta","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYBCDBAYe5gNAWkKGFC1sCSAtPKRo4TEAMQhrkW/vPcBc2HYvj7/nzOdXN2oseBjYDx/dgE+LwZlzCcwz24qLJc72brPOOQZ0GE9a2g28WiRyDJh52xISG87zbjPOYQNqkeAxw6tFfv4biJb553meGef8I0ILww0eiJYNZ3uYH+e2EaHF4EyOweEZ5xKKDc8cM2PO7ZPgYSPkF/n2M4aPC8oS8uTOJD/+nPOtTo6f/fAx/A4DgsNQmk0CTBJSDgLMMPoDMapHwSgYBaNg5AEA405FgATmbfwAAAAASUVORK5CYII=","orcid":"","institution":"Aarhus University","correspondingAuthor":true,"prefix":"","firstName":"Purbak","middleName":"","lastName":"Sengupta","suffix":""},{"id":526716064,"identity":"356e16a6-c918-42c8-b67f-55f88fe1ac7b","order_by":1,"name":"Sambeet Mishra","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYHACNgjFDGbYEK3FAKYljRQtEMZhwurl20+nPfhQ8YdBt5392eOKmvOJ82c3sD34gEeLwZnc7YYzzhgwmB3mMTc8c+x24oY7B9gNZ+DTwpC7TZq3DayFTbKBDahFIoFNmgefw/rfbpP++w+khf2ZZMO/c4nzZxDQwnADaAtjA0gLg5lkY9uBxIYbBLQY3Hi73bDnmDEP0GFALX3JxhtuJLZJ4vOLfH/utgc/auTkzM4fBzrsm53s/BnJxyTwhRgMILuEsYEIDaNgFIyCUTAK8AEAbc5Lh/DO01MAAAAASUVORK5CYII=","orcid":"","institution":"University of South-Eastern Norway","correspondingAuthor":true,"prefix":"","firstName":"Sambeet","middleName":"","lastName":"Mishra","suffix":""}],"badges":[],"createdAt":"2025-10-08 21:19:10","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7810964/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7810964/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93260707,"identity":"3e6e41e5-797b-46ca-96f6-7a5c0482a86e","added_by":"auto","created_at":"2025-10-10 18:08:27","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":474877,"visible":true,"origin":"","legend":"","description":"","filename":"EnhancedRBAmethodforuncertainityquantificationintimevaryingdatasetafterreview.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7810964/v1_covered_c3d2e569-08d4-4a8b-9f80-d826152005e3.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eEnhanced RBA\u003csub\u003eθ\u003c/sub\u003e method for uncertainty quantification in time varying dataset\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of South-Eastern Norway","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"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":"Time-series variation, uncertainty quantification, wind power, adaptive thresholding, ramping behavior analysis, machine learning, Random Forest, Bayesian inference, event detection, renewable energy systems","lastPublishedDoi":"10.21203/rs.3.rs-7810964/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7810964/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe high velocity and variability of wind power data introduce challenges in reliably detecting ramp events and quantifying uncertainty. To address this, we propose the enhanced Ramping Behavior Analysis (RBAθ ) framework, which extends the original RBAθ method by introducing two adaptive thresholding strategies: a statistical inference-based threshold and a Random Forest–based Markov Chain Monte Carlo (RF–MCMC) threshold. These replace static thresholds with data-driven, uncertainty-aware mechanisms. Empirical evaluation on wind capacity factor datasets shows that the enhanced RBAθ -Traditional (statistical thresholding) achieves an overall performance score of 0.82, with perfect robustness (1.00) and strong balance (0.94). Compared to existing approaches, it improves consistency by 20.7% over Sliding Window Ramp Threshold (SWRT) and balance by 88.8% over Cumulative Sum (CUSUM), while enabling reliable detection of both significant and stationary ramp events. 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