[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123771-en":3,"doc-seo-123771-105":30,"detail-sidebar-cat-0-en-105":95},{"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":20,"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},123771,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Machine learning-based approach to predict thermal comfort in mixed-mode buildings - incorporating adaptive behaviors","Mixed-mode (MM) buildings use mechanical air conditioning alongside natural passive cooling, aiming to widen occupants’ acceptable comfort range beyond the narrow limits typical of HVAC systems. Evidence suggests occupants in MM buildings display stronger thermal adaptation, yet the relationship between expanded comfort ranges and specific adaptive behaviors or contextual drivers remains insufficiently addressed. This study evaluates how occupants’ adaptive behaviors affect thermal comfort using one-year field data from two MM office buildings and machine learning models. Results show that adding adaptive behaviors improves overall model performance, while PMV offers limited accuracy but stronger recall. The findings also identify energy-inefficient adaptations during HVAC operation, such as air conditioning in mild seasons and frequent window openings during summer cooling.","Machine learning-based approach to predict thermal comfort in mixed-mode buildings: incorporating adaptive behaviors  \nArticle  \nAccepted Version  \nCreative Commons: Attribution-Noncommercial-No Derivative Works 4.0  \nZhang, S., Yao, R. ORCID: [https://orcid.org/0000-0003-4269-](https://orcid.org/0000-0003-4269-)[ ](https://orcid.org/0000-0003-4269-)7224, Toftum, J., Essah, E. ORCID: [https://orcid.org/0000-](https://orcid.org/0000-)[ ](https://orcid.org/0000-)[0002-1349-5167 and Li](0002-1349-5167 and Li) , B. (2024) Machine learning-based approach to predict thermal comfort in mixed-mode buildings:  \nincorporating adaptive behaviors. Journal of Building Engineering, 87. 108877. ISSN 2352-7102 doi:  \n[https://doi.org/10.1016/j.jobe.2024.108877 Available](https://doi.org/10.1016/j.jobe.2024.108877 Available) at  \n[https://centaur. reading.ac. uk/1](https://centaur. reading.ac. uk/1) 15742/  \nIt is advisable to refer to the publisher’s version if you intend to cite from the work. See Guidance on citing.  \nTo link to this article DOI: [http://dx.doi.org/10.1016/j.jobe.2024.108877](http://dx.doi.org/10.1016/j.jobe.2024.108877)  \nPublisher: Elsevier  \nAll outputs in CentAUR are protected by Intellectual Property Rights law, including copyright law. Copyright and IPR is retained by the creators or other copyright holders . Terms and conditions for use of this material are defined in the End User Agreement  .  \n[www. reading.ac. uk/centaur](www. reading.ac. uk/centaur)  \nCentAUR  \nCentral Archive at the University of Reading  \nReading’s research outputs online  \nMachine learning-based approach to predict thermal comfort in mixed-mode buildings: Incorporating adaptive behaviors  \nShaoxing Zhang a, b, Runming Yao a, b, *, Jørn Toftum c, Emmanuel Essah b, Baizhan Li  \na  \na Joint International Research Laboratory of Green Buildings and Built Environments (Ministry of Education), Chongqing University, Chongqing, 400045, China  \nb School of the Built Environment, University of Reading, UK  \nc Section for Indoor Environment, Department of Civil Engineering, Technical University of Denmark, Denmark  \nAbstract  \nMixed-mode (MM) buildings are designed to provide mechanical air conditioning and natural passive cooling as regulated by occupants. This would enable the potential of shifting the narrow comfort range in HVAC (heating, ventilation and air conditioning) buildings to a wider range similar to NV (naturally ventilated) buildings. Recent studies have provided evidence of higher degrees of thermal adaptation among occupants in MM buildings. However, limited attention has been given to understanding the linkages between these expanded ranges and the specific adaptive behaviors or contextual factors that influence them. This paper aims to investigate the influence of occupants’adaptive behaviors on thermal comfort in MM buildings. A one-year field study in two MM office buildings with 5,096 valid questionnaires was conducted in Chongqing, China, under hot summer and cold winter climatic characteristics by developing machine learning algorithms compared with classic thermal comfort models. Results show that incorporating adaptive behaviors as input variables enhances the performance of machine learning algorithms, leading to improved overall model performance, while the classic thermal comfort index PMV (predictive mean vote) presents the limited accuracy but the best recall in most cases. This paper also demonstrates that some  \nenergy-inefficient thermal adaptations were found in MM buildings during the HVAC mode, such as using air conditioning in mild spring and autumn, and frequent window openings during cooling periods of summer. It is therefore valuable for future research to further focus on how MM buildings both incorporate positive features and reduce negative features during the HVAC and NV modes.  \nKeywords: Adaptive thermal comfort, PMV, Adaptive model, Adaptive behaviors, Machine learning  \nAbbreviations  \nAC Air conditioning C","cbCait6csClG6qsX","https://ap.wps.com/l/cbCait6csClG6qsX","pdf",2819505,1,68,"English","en",105,"# Introduction\n## Mixed-mode buildings and thermal comfort models\n## Study aim and research approach\n# Methods\n## Field study design and data collection\n## Machine learning algorithms and comparison models\n# Results\n## Model performance with adaptive behavior inputs\n## Insights from PMV and adaptation patterns\n# Discussion\n## Positive adaptations and energy-inefficient behaviors\n## Future research directions","[{\"question\":\"What is the main goal of this paper on thermal comfort in mixed-mode buildings?\",\"answer\":\"To investigate how occupants’ adaptive behaviors influence thermal comfort in mixed-mode buildings and how this affects predictive performance of comfort models.\"},{\"question\":\"How was the study conducted and what data were used?\",\"answer\":\"A one-year field study was carried out in two mixed-mode office buildings in Chongqing, China, using 5,096 valid questionnaire responses collected under hot-summer and cold-winter conditions.\"},{\"question\":\"What impact does including adaptive behaviors have on machine learning predictions?\",\"answer\":\"Incorporating adaptive behaviors as input variables improves the overall performance of machine learning algorithms compared with classic thermal comfort models.\"},{\"question\":\"What energy-inefficient adaptive behaviors were observed during HVAC mode?\",\"answer\":\"The study reports using air conditioning during mild spring and autumn periods and frequent window openings during summer cooling periods.\"}]","Machine learning-based approach to predict thermal comfort in mixed-mode buildings - incorporating adaptive behaviors | PDF",1785818460,171,{"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":90,"head_meta":92,"extra_data":94,"updated_unix":28},"machine-learning-based-approach-to-predict-thermal-comfort-in-mixed-mode-buildings-incorporating-adaptive-behaviors","",{"@graph":36,"@context":89},[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/machine-learning-based-approach-to-predict-thermal-comfort-in-mixed-mode-buildings-incorporating-adaptive-behaviors/123771/",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","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of this paper on thermal comfort in mixed-mode buildings?","Question",{"text":75,"@type":76},"To investigate how occupants’ adaptive behaviors influence thermal comfort in mixed-mode buildings and how this affects predictive performance of comfort models.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the study conducted and what data were used?",{"text":80,"@type":76},"A one-year field study was carried out in two mixed-mode office buildings in Chongqing, China, using 5,096 valid questionnaire responses collected under hot-summer and cold-winter conditions.",{"name":82,"@type":73,"acceptedAnswer":83},"What impact does including adaptive behaviors have on machine learning predictions?",{"text":84,"@type":76},"Incorporating adaptive behaviors as input variables improves the overall performance of machine learning algorithms compared with classic thermal comfort models.",{"name":86,"@type":73,"acceptedAnswer":87},"What energy-inefficient adaptive behaviors were observed during HVAC mode?",{"text":88,"@type":76},"The study reports using air conditioning during mild spring and autumn periods and frequent window openings during summer cooling periods.","https://schema.org",{"og:url":52,"og:type":91,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":93,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":96},[97,101,105,109,114,119,124,127,132,135,139],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Exam",70,"exam",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},5,"Comic",60,"comic",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},6,"Technology",50,"technology",{"id":120,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":122,"slug":123},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":125,"slug":126},30,"research-report",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":130,"slug":131},9,"Religion & Spirituality",20,"religion-spirituality",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":130,"slug":134},"World Cup","world-cup",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":136,"slug":138},10,"Lifestyle","lifestyle",{"id":140,"doc_module":4,"doc_module_name":46,"category_name":141,"show_sort_weight":110,"slug":142},19,"General","general"]