[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120630-en":3,"doc-seo-120630-105":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},120630,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","Understanding thermal comfort using self-reporting and interpretable machine learning","Standard thermal comfort models often fail to reflect individual thermal sensations and provide limited interpretability for real-world decision making. This research introduces a building-specific, occupant-centered framework that fuses self-reported comfort data with interpretable machine learning. A summer–winter case study builds a random forest regression model and applies interpretable methods including partial dependence plots, SHAP values, and surrogate models. The results support insight into comfort drivers and enable targeted energy-saving and satisfaction-oriented interventions while acknowledging limitations of subjective inputs and missing architectural features.","Understanding thermal comfort using self-reporting and interpretable machine learning  \nCitation for published version (APA):  \nUpasani, N. , Guerra-Santin, O. , Mohammadi, M. , Seraj, M. , & Joosstens, F. (2025) . Understanding thermal comfort using self-reporting and interpretable machine learning. Energy Efficiency, 18(7), Article 74. [https://doi.org/10.1007/s12053-025-10371-9](https://doi.org/10.1007/s12053-025-10371-9)  \nDOI:  \n10.1007/s12053-025-10371-9  \nDocument status and date:  \nPublished: 01/10/2025  \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: 28. Apr. 2026  \nEnergy Efficiency (2025) 18:74  \n[https://doi.org/10.1007/s12053-025-10371-9](https://doi.org/10.1007/s12053-025-10371-9)  \nUnderstanding thermal comfort using self‑reporting and interpretable machine learning  \nNitant Upasani · Olivia Guerra‑Santin · Masi Mohammadi · Mazyar Seraj · Frans Joosstens  \nReceived: 8 May 2024 / Accepted: 13 August 2025 © The Author(s) 2025  \nAbstract Standard thermal comfort models often fail to capture individual thermal sensations and offer limited interpretability for practical use. This study presents a building-specific, occupant-centric approach that combines self-reported comfort data with interpretable machine learning. The methodology is demonstrated through a case study involving self-reporting campaigns conducted during summer and winter seasons, accompanied by the development of a random forest regression (RFR) model. We employ three IML techniques namely Partial Dependence Plots (PDPs), SHAP values, and surrogate models to enhance the understanding of this RFR model.  \nN. Upasani (*) · O. Guerra-Santin · M. Mohammadi Chair Smart Architectural Technologies, Department of the Built Environment, Eindhoven University of Technology, Den Dolech 2, 5612 AZ Eindhoven, The Netherlands  \ne-mail: [n.a.upasani@tue.nl](n.a.upasani@tue.nl)  \nM. Mohammadi  \nResearch Group Architecture in Health, HAN University of Applied Sciences, Ruitenberglaan 26, 6826 CC Arnhem, The Netherlands  \nM. Seraj  \nDepartment of Mathematics and Computer Science (M&CS), Eindhoven University of Technology, Den Dolech 2, 5612 AZ Eindhoven, The Netherlands  \nF. Joosstens  \nDepartment of Facili","cbCairUohg8L4NJl","https://ap.wps.com/l/cbCairUohg8L4NJl","pdf",3410227,1,30,"English","en",105,"# Abstract\n# Introduction\n## Motivation: intelligent building control and energy use\n## Limits of traditional comfort models\n## Study approach and interpretable modeling","[{\"question\":\"Why are traditional thermal comfort models insufficient for practical use?\",\"answer\":\"They generalize comfort and do not adequately capture individual differences such as gender, age, physiology, location, and building characteristics. They also offer limited interpretability for decision making.\"},{\"question\":\"What does the proposed methodology combine to model thermal comfort?\",\"answer\":\"The framework combines occupant self-reported comfort data with interpretable machine learning to create insights that connect comfort drivers to model behavior.\"},{\"question\":\"Which interpretable machine learning techniques are used with the random forest regression model?\",\"answer\":\"Partial dependence plots (PDPs), SHAP values, and surrogate models are employed to enhance understanding of the model and the factors influencing thermal comfort.\"}]","Understanding thermal comfort using self-reporting and interpretable machine learning | PDF",1785730990,76,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"understanding-thermal-comfort-using-self-reporting-and-interpretable-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/understanding-thermal-comfort-using-self-reporting-and-interpretable-machine-learning/120630/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are traditional thermal comfort models insufficient for practical use?","Question",{"text":75,"@type":76},"They generalize comfort and do not adequately capture individual differences such as gender, age, physiology, location, and building characteristics. They also offer limited interpretability for decision making.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What does the proposed methodology combine to model thermal comfort?",{"text":80,"@type":76},"The framework combines occupant self-reported comfort data with interpretable machine learning to create insights that connect comfort drivers to model behavior.",{"name":82,"@type":73,"acceptedAnswer":83},"Which interpretable machine learning techniques are used with the random forest regression model?",{"text":84,"@type":76},"Partial dependence plots (PDPs), SHAP values, and surrogate models are employed to enhance understanding of the model and the factors influencing thermal comfort.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":21,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]