[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123611-en":3,"doc-seo-123611-105":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},123611,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","A data-driven energy performance gap prediction model using machine learning","The energy performance gap creates a major barrier to building decarbonization goals, leaving decision-makers with limited guidance on where and why prediction failures may occur. This study places project risks at the center of the gap and introduces a machine learning classification-based prediction approach for building heating and electricity demand. Data from 77 buildings were collected through a web-based survey, and four algorithms were compared to select the most reliable model. Results show Naive Bayes and additional models can forecast gap direction across heating and electricity and support practical gap assessment.","A data-driven energy performance gap prediction model using machine learning Article  \nAccepted Version  \nCreative Commons: Attribution-Noncommercial-No Derivative Works 4.0  \nYilmaz, D. , Tanyer, A. M. and Toker, İ . D. ORCID: [https://orcid.org/0000-0002-6988-7557](https://orcid.org/0000-0002-6988-7557) (2023) A data-driven energy performance gap prediction model using machine learning. Renewable and Sustainable Energy Reviews, 181.  \n113318. ISSN 1879-0690 doi:  \n[https://doi.org/10.1016/j. rser.2023.113318 Available](https://doi.org/10.1016/j. rser.2023.113318 Available) at  \n[https://centaur. reading.ac. uk/1](https://centaur. reading.ac. uk/1) 11874/  \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. rser.2023.113318](http://dx.doi.org/10.1016/j. rser.2023.113318)  \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  \nA data-driven energy performance gap prediction model using machine learning  \nYılmaz, D.a, Tanyer, A.M.b, *, Toker Dikmen, I.c,1  \na Department of Architecture, Middle East Technical University, Ankara, 06800, Turkey  \nb Department of Architecture, Research Center for Built Environment, Middle East Technical University, Ankara, 06800, Turkey  \nc Department of Civil Engineering, Middle East Technical University, Ankara, 06800, Turkey  \nAbstract  \nThe energy performance gap is a significant obstacle to the realization of ambitions to mitigate the environmental impact of buildings. Although extensive research has been conducted on the causes, minimization, or the quantifying of the energy performance gap in buildings, comparatively minimal work has been done on raising decision-makers awareness of a potential gap.  \nThis paper positions project risks at the core of the gap and proposes an innovative performance gap prediction model focusing on heating and electricity demand in buildings by utilizing the machine learning classification. In this research, the performance gap and project risks of 77 buildings was collected via a web-based survey. The predictive performance of the four machine learning algorithms, namely i) Naive Bayes, ii) k-Nearest Neighbors, iii) Support Vector Machine, and iv) Random Forest, were compared to determine the best model.  \nThe results obtained revealed that Naive Bayes was better able to predict the direction of the heating performance gap (72.50%), the negative heating performance gap (71.81%), the positive electricity performance gap (77.08%), and the negative electricity performance gap (83.85%) . Furthermore, k-Nearest Neighbors and Support Vector Machine were more accurate to predict the direction of the electricity performance gap (79.00%), and the positive heating performance gap (76.04%) .  \nHighlights  \n• A performance gap prediction model was proposed based on buildings’ risk data.  \n• The models use machine learning to focus on the electricity and heating gaps.  \n* Corresponding author.  \nE-mail address: [tanyer@metu.edu.tr](tanyer@metu.edu.tr) (A.M.Tanyer)  \n1 Present address: School of Construction Management and Engineering, University of Reading, Reading, RG6 6EN, United Kingdom  \n• The performance of four machine learning algorithms was compared.  \n• The suggested method can predict the direction of the gap (positive and negative) .  \n• The suggested method can predict the gap in three levels (low, medium, high) .  \nKeywords  \nAlgorithm, building, classification, energy performance gap, machine learning, risk identification  \nWord Count: 7572  \n\n| Nomenclature\u003Cbr","cbCaim7T63HsQLcE","https://ap.wps.com/l/cbCaim7T63HsQLcE","pdf",1255016,1,35,"English","en",105,"# Abstract\n# Highlights\n# Keywords\n# Introduction\n## Energy performance gap and its significance\n## Prediction vs. measured building performance","[{\"question\":\"What problem does the energy performance gap create for buildings?\",\"answer\":\"It represents the difference between predicted performance in the design phase and measured performance in the operational phase, hindering efforts to meet environmental and energy-reduction ambitions.\"},{\"question\":\"How does the proposed model incorporate project risks?\",\"answer\":\"It positions project risks as central to the gap and uses machine learning classification to predict the energy performance gap for building heating and electricity demand.\"},{\"question\":\"Which machine learning algorithms were compared, and what were the key findings?\",\"answer\":\"Naive Bayes, k-Nearest Neighbors, Support Vector Machine, and Random Forest were compared. Naive Bayes performed especially well in predicting heating-gap direction and electricity positive/negative gap classes, while k-Nearest Neighbors and SVM showed strong accuracy for electricity gap direction.\"}]","A data-driven energy performance gap prediction model using machine learning | PDF",1785817631,88,{"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},"a-data-driven-energy-performance-gap-prediction-model-using-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/a-data-driven-energy-performance-gap-prediction-model-using-machine-learning/123611/",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":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the energy performance gap create for buildings?","Question",{"text":75,"@type":76},"It represents the difference between predicted performance in the design phase and measured performance in the operational phase, hindering efforts to meet environmental and energy-reduction ambitions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed model incorporate project risks?",{"text":80,"@type":76},"It positions project risks as central to the gap and uses machine learning classification to predict the energy performance gap for building heating and electricity demand.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning algorithms were compared, and what were the key findings?",{"text":84,"@type":76},"Naive Bayes, k-Nearest Neighbors, Support Vector Machine, and Random Forest were compared. Naive Bayes performed especially well in predicting heating-gap direction and electricity positive/negative gap classes, while k-Nearest Neighbors and SVM showed strong accuracy for electricity gap direction.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]