[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120339-en":3,"doc-seo-120339-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120339,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Automated machine learning-based building energy load prediction method","Data-driven building energy load prediction depends heavily on human expertise for model training, making practical deployment time-consuming. This study introduces an automated machine learning approach that builds accurate data-driven models with minimal human assistance. Six representative AutoML frameworks (AutoWeka, H2O, TPOT, AutoGluon, FLAML, and AutoKeras) are evaluated for 1-hour-ahead heating, cooling, and electrical load forecasting across three real buildings. Results indicate the proposed method outperforms manual modeling, with accuracy gains from 1.10% to 18.66%, and show AutoGluon/FLAML can achieve strong accuracy with short training times while AutoKeras underperforms, providing usage guidelines for real applications.","Automated machine learning-based building energy load prediction method  \nCitation for published version (APA):  \nZhang, C. , Tian, X. , Zhao, Y. , & Lu, J. (2023) . Automated machine learning-based building energy load prediction method. Journal of Building Engineering , 80, Article 108071.  \n[https://doi.org/10.1016/j.jobe.2023.108071](https://doi.org/10.1016/j.jobe.2023.108071)  \nDocument license:  \nTAVERNE  \nDOI:  \n10.1016/j.jobe.2023.108071  \nDocument status and date:  \nPublished: 01/12/2023  \nDocument Version:  \nPublisher’s PDF, also known as Version of Record (includes final page, issue and volume numbers)  \nPlease check the document version of this publication:  \n• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.  \n• The final author version and the galley proof are versions of the publication after peer review.  \n• The final published version features the final layout of the paper including the volume, issue and page numbers.  \nLink to publication  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \n• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  \n• You may not further distribute the material or use it for any profit-making activity or commercial gain  \n• You may freely distribute the URL identifying the publication in the public portal.  \nIf the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license above, please follow below link for the End User Agreement:  \n[www.tue.nl/taverne](www.tue.nl/taverne)  \nTake down policy  \nIf you believe that this document breaches copyright please contact us at:  \n[openaccess@tue.nl](openaccess@tue.nl)  \nproviding details and we will investigate your claim.  \nDownload date: 08. Sep. 2025  \nJournal of Building Engineering 80 (2023) 108071  \nContents lists available at ScienceDirect  \nJournal of Building Engineering  \njournal [homepage: www.elsevier.com/locate/jobe](homepage: www.elsevier.com/locate/jobe)  \n| Automated machine learning-based building energy load prediction method |  |  |\n| --- | --- | --- |\n| Chaobo Zhang a, b, Xiangning Tianc, d, Yang Zhao a, e, *, Jie Lua\u003Cbr>a Institute of Refrigeration and Cryogenics, Zhejiang University, Hangzhou, China\u003Cbr>b Department of the Built Environment, Eindhoven University of Technology, Eindhoven, the Netherlands c Center for Balance Architecture, Zhejiang University, Hangzhou, China\u003Cbr>d Architectural Design & Research Institute of Zhejiang University Co., Ltd., Hangzhou, China\u003Cbr>e Key Laboratory of Clean Energy and Carbon Neutrality of Zhejiang Province, Jiaxing Research Institute, Zhejiang University, Jiaxing, China |  |  |\n| A R T I C L E I N F O\u003Cbr>Keywords:\u003Cbr>Building energy load prediction Data-driven models Automated machine learning Manual modeling\u003Cbr>Building energy efficiency enhancement | A B S T R A C T\u003Cbr>The application of data-driven building energy load prediction technologies remains a timeconsuming effort, since it highly relies on human expertise to train data-driven building energy load prediction models. To address this issue, this study proposes an automated machine learningbased method which can develop accurate data-driven building energy load prediction models with little human assistance. The potential of six representative automated machine learning frameworks (AutoWeka, H2O, TPOT, AutoGluon, FLAML and AutoKeras) are in","cbCaiatZGyboQIhi","https://ap.wps.com/l/cbCaiatZGyboQIhi","pdf",3197104,1,19,"English","en",105,"# Abstract\n# Keywords\n# Nomenclature\n## Automated machine learning frameworks\n## Error metrics and evaluation terms","[{\"question\":\"Why is building energy load prediction difficult to deploy using data-driven models?\",\"answer\":\"Because training data-driven building energy load prediction models relies strongly on human expertise, which makes the process time-consuming.\"},{\"question\":\"What AutoML frameworks were investigated for forecasting?\",\"answer\":\"The study evaluates AutoWeka, H2O, TPOT, AutoGluon, FLAML, and AutoKeras for 1-hour-ahead heating, cooling, and electrical load prediction.\"},{\"question\":\"How did the automated method perform compared with manual modeling?\",\"answer\":\"It outperformed manual modeling, improving prediction accuracy by 1.10%–18.66%, and AutoGluon and FLAML achieved high accuracy with short training times.\"}]","Automated machine learning-based building energy load prediction method | 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is building energy load prediction difficult to deploy using data-driven models?","Question",{"text":75,"@type":76},"Because training data-driven building energy load prediction models relies strongly on human expertise, which makes the process time-consuming.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What AutoML frameworks were investigated for forecasting?",{"text":80,"@type":76},"The study evaluates AutoWeka, H2O, TPOT, AutoGluon, FLAML, and AutoKeras for 1-hour-ahead heating, cooling, and electrical load prediction.",{"name":82,"@type":73,"acceptedAnswer":83},"How did the automated method perform compared with manual modeling?",{"text":84,"@type":76},"It outperformed manual modeling, improving prediction accuracy by 1.10%–18.66%, and AutoGluon and FLAML achieved high accuracy with short training 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