[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125506-en":3,"doc-seo-125506-105":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},125506,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","Intrinsically interpretable machine learning-based building energy load prediction method with high accuracy and strong interpretability","Black-box models achieve strong accuracy for building energy load forecasting but often lack interpretability and fail to embed domain knowledge, limiting user trust in practice. This study proposes an intrinsically interpretable machine learning framework combining clustering decision trees with adaptive multiple linear regression. The method identifies building operating conditions to train tailored submodels and improves nonlinear fitting via adaptive regression coefficients. Evaluation on office-building operational data shows accuracy comparable to random forests and extreme gradient boosting and about 10.2% average improvement over several popular black-box methods.","Intrinsically interpretable machine learning-based building energy load prediction method with high accuracy and strong interpretability  \nCitation for published version (APA):  \nZhang, C. , Hoes, P.-J. , Wang, S. , & Zhao, Y. (2026) . Intrinsically interpretable machine learning-based building energy load prediction method with high accuracy and strong interpretability. Energy and Built Environment, 7(1), 94-114 . [https://doi.org/10.1016/j.enbenv.2024.08.006](https://doi.org/10.1016/j.enbenv.2024.08.006)  \nDocument license:  \nCC BY-NC-ND  \nDOI:  \n10.1016/j.enbenv.2024.08.006  \nDocument status and date:  \nPublished: 01/02/2026  \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: 26. Apr. 2026  \nEnergy and Built Environment 7 (2026) 94–114  \nContents lists available at ScienceDirect  \nEnergy and Built Environment  \njournal homepage: [http://www.keaipublishing.com/en/journals/energy-and-built-environment/](http://www.keaipublishing.com/en/journals/energy-and-built-environment/)  \n| Full Length Article\u003Cbr>Intrinsically interpretable machine learning-based building energy load prediction method with high accuracy and strong interpretability\u003Cbr>Chaobo Zhang a, Pieter-Jan Hoes a, Shuwei Wang a, Yang Zhao b,c,∗\u003Cbr>a Department of the Built Environment, Eindhoven University of Technology, Eindhoven, the Netherlands b Institute of Refrigeration and Cryogenics, Zhejiang University, Hangzhou, China\u003Cbr>c Key Laboratory of Clean Energy and Carbon Neutrality of Zhejiang Province, Jiaxing Research Institute, Zhejiang University, Jiaxing, China |  |  |\n| --- | --- | --- |\n| a r t i c l e i n f o |  | a b s t r a c t |\n| Keywords:\u003Cbr>Interpretable machine learning Intrinsic interpretability\u003Cbr>Building energy load prediction Clustering decision trees Adaptive multiple linear regression |  | Black-box models have demonstrated remarkable accuracy in forecasting building energy loads. However, they usually lack interpretability and do not incorporate domain knowledge, making it difficult for users to trust their predictions in practical applications. One important and interesting question remains unanswered: is it possible to use intrinsically interpretable models to achieve accuracy compa","cbCaifGd3qNp7USM","https://ap.wps.com/l/cbCaifGd3qNp7USM","pdf",7087712,1,22,"English","en",105,"# Introduction\n## Method Overview: Intrinsic Interpretability Framework\n## Algorithms: Clustering Decision Trees and Adaptive Multiple Linear Regression\n## Experimental Evaluation on Office Building Data\n## Results: Accuracy Comparison and Interpretability Insights\n## Future Work","[{\"question\":\"Why focus on intrinsically interpretable models for building energy load prediction?\",\"answer\":\"Black-box models can be accurate but are hard to interpret and do not incorporate domain knowledge, which undermines trust in real applications.\"},{\"question\":\"How does the proposed method combine clustering decision trees and adaptive multiple linear regression?\",\"answer\":\"Clustering decision trees identify distinct building operating conditions so multiple tailored models can be trained, while adaptive multiple linear regression adjusts coefficients to better capture nonlinear relationships.\"},{\"question\":\"What predictors and time patterns are identified as most important for cooling load under conditions?\",\"answer\":\"Historical cooling loads are the most crucial for predicting cooling loads under most conditions, and outdoor air temperature significantly contributes during daytime on summer and transition-season weekdays.\"}]","Intrinsically interpretable machine learning-based building energy load prediction method with high accuracy and strong interpretability | 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