Electricity Consumption Behavior and Load Forecasting Analysis Coupling Meteorological Factors Using BK-Means and NRBO-XGBoost Algorithms

Electricity Consumption Behavior and Load Forecasting Analysis Coupling Meteorological Factors Using BK-Means and NRBO-XGBoost Algorithms | 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 Electricity Consumption Behavior and Load Forecasting Analysis Coupling Meteorological Factors Using BK-Means and NRBO-XGBoost Algorithms Nantian Huang, Jingyuan Zhang, Shicheng Ren, Hao Zhang, BingLing Li, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6920221/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 To address the challenges of extracting user electricity consumption behavior features and insufficient load prediction accuracy in multi-energy coupling scenarios, this study proposes an electricity behavior analysis and forecasting methodology integrating data cleansing with meteorological correlations. Firstly, the Akima interpolation method is employed to rectify abnormal load data points, combined with a highly robust Z-M-ESD algorithm (Z-score Median-based Extreme Studentized Deviate) incorporating median identification and seasonal adjustment for iterative data cleansing, achieving an average 64.765% reduction in outlier correction errors. Secondly, BIRCH pre-clustering is utilized to adaptively determine optimal cluster numbers and initial centroids, thereby improving the traditional K-Means algorithm for joint meteorological clustering analysis of wind-photovoltaic power outputs and load coupling, as well as user clustering analysis. This enhancement elevates the user classification silhouette coefficient to 0.4679, representing a 24.04% improvement over conventional methods. Finally, an NRBO-XGBoost based load forecasting model incorporating meteorological parameters such as temperature and humidity is developed. Experimental results demonstrate that the proposed approach reduces the root mean square error to 49.2 kW and mean absolute error to 38.6 kW when considering meteorological factors. This methodology provides a theoretical foundation for demand-side management in power systems. Akima Interpolation Filling BK-Means algorithm NRBO-XGBoost algorithm Load Forecasting 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. 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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-6920221","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":473155386,"identity":"04a69d29-6762-420e-94a9-220754fc5ad5","order_by":0,"name":"Nantian 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