A short-term load forecasting framework for air conditioning system based on model stacking

A short-term load forecasting framework for air conditioning system based on model stacking | 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 A short-term load forecasting framework for air conditioning system based on model stacking Tianjie Liu, Wenling Jiao, Zhiwei Huang, Jianting Yu, Xin Zhang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5352056/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Accurate short-term air conditioning load forecasting is very important for controlling air conditioning energy consumption, and is also a requisite for realizing intelligent control of air conditioning units. This paper proposes a short-term load forecasting framework for air conditioning based on the concept of model stacking, which combines six mature machine learning models, including Lasso regression, Ridge regression, Random Forest, Support Vector Regression, eXtreme Gradient Boosting and Long Short-Term Memory, and trains new prediction models through model stacking. In order to realize the short term forecast function of air conditioning load, this paper presents a complete forecast framework, which includes operational procedures for feature screening, algorithm hyperparameters optimization, and cross stacking of prediction models. A real air conditioning system is employed for prediction analysis, the prediction results showed that 28 out of 36 control simulations achieved better prediction accuracy, with an average increase in R 2 of 6.4%. Notably, simpler submodels in the meta-model yield better results in model stacking, whereas complex coupling models as the meta-model may degrade performance. These findings provide insights into implementation of model stacking and selecting the meta-model for short-term air conditioning load forecasting. Physical sciences/Engineering Physical sciences/Engineering/Civil engineering Air conditioning system Load forecasting Model stacking Hyperparameter optimization Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 22 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 19 Feb, 2025 Reviews received at journal 25 Jan, 2025 Reviewers agreed at journal 12 Jan, 2025 Reviews received at journal 17 Dec, 2024 Reviewers agreed at journal 07 Dec, 2024 Reviewers agreed at journal 03 Dec, 2024 Reviewers invited by journal 17 Nov, 2024 Editor assigned by journal 17 Nov, 2024 Editor invited by journal 15 Nov, 2024 Submission checks completed at journal 14 Nov, 2024 First submitted to journal 29 Oct, 2024 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-5352056","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":387480808,"identity":"ee65e3bd-8758-42bb-93f3-c6ac0bad471f","order_by":0,"name":"Tianjie Liu","email":"","orcid":"","institution":"Shenzhen Gas Corporation Ltd.","correspondingAuthor":false,"prefix":"","firstName":"Tianjie","middleName":"","lastName":"Liu","suffix":""},{"id":387480809,"identity":"5b0a68eb-14ec-433c-8be8-057b852b6b9a","order_by":1,"name":"Wenling 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