[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118897-en":3,"doc-seo-118897-105":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},118897,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Urban Building Energy Performance Prediction and Retrofit Analysis - Data-Driven Machine Learning Approach","Urban building energy performance modeling supports planners and policymakers in designing strategic sustainable energy plans to curb energy use and emissions. A persistent gap remains due to inconsistent energy datasets and limited scalability of building models, while large-scale energy surveys are time-consuming. Existing studies often rely on traditional statistical or conventional machine learning methods. The proposed work applies data-driven machine learning combining ensemble learning with end-use demand segregation to predict urban residential building energy performance.","Urban building energy performance prediction and retrofit analysis using data-driven machine learning approach  \nAli, U. , Hewitt, N. , Bano, S. , Shamsi, M. , Sood, D. , Hoare, C. , Zuo, W. , & O'Donnell, J. (2024) . Urban building energy performance prediction and retrofit analysis using data-driven machine learning approach. Energy and Buildings, 303, 1-16 . Article 113768. Advance online publication. [https://doi.org/10.1016/j.enbuild.2023.113768](https://doi.org/10.1016/j.enbuild.2023.113768)  \nLink to publication record in Ulster University Research Portal  \nPublished in:  \nEnergy and Buildings  \nPublication Status:  \nPublished online: 15/01/2024  \nDOI:  \n10.1016/j.enbuild.2023.113768  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nGeneral rights  \nCopyright for the publications made accessible via Ulster University's Research Portal is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights.  \nTake down policy  \nThe Research Portal is Ulster University's institutional repository that provides access to Ulster's research outputs. Every effort has been made to ensure that content in the Research Portal does not infringe any person's rights, or applicable UK laws. If you discover content in the Research Portal that you believe breaches copyright or violates any law, please contact [pure-support@ulster.ac.uk](pure-support@ulster.ac.uk).  \nDownload date: 12/01/2024  \nEnergy & Buildings 303 (2024) 113768  \nContents lists available at ScienceDirect  \nEnergy & Buildings  \njournal [homepage: www.elsevier.com/locate/enbuild](homepage: www.elsevier.com/locate/enbuild)  \n| Urban building energy performance prediction and retrofit analysis using data-driven machine learning approach\u003Cbr>Usman Ali a,∗ , Sobia Bano a, Mohammad Haris Shamsi d, Divyanshu Sood a, Cathal Hoare a, Wangda Zuo c, Neil Hewitt b, James O’Donnell a\u003Cbr>a School of Mechanical and Materials Engineering and UCD Energy Institute, UCD, Dublin, Ireland b School of Architecture and The Built Environment, Ulster University, Belfast, UK c Pennsylvania State University, University Park, PA, USA\u003Cbr>d Flemish Institute for Technological Research (VITO), Boeretang Mol, Belgium |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>Building energy performance Data-driven approaches\u003Cbr>Urban building energy modeling Machine learning\u003Cbr>Building retrofit |  | Stakeholders such as urban planners and energy policymakers use building energy performance modeling and analysis to develop strategic sustainable energy plans with the aim of reducing energy consumption and emissions from the built environment. However, inconsistent energy data and the lack of scalable building models create a gap between building energy modeling and traditional planning practices. An alternative approach is to conduct a large-scale energy usage survey, which is time-consuming. Similarly, existing studies rely on traditional machine learning or statistical approaches for calculating large-scale energy performance. This paper proposes a solution that employs a data-driven machine learning approach to predict the energy performance of urban residential buildings, using both ensemble-based machine learning and end-use demand segregation methods. The proposed methodology consists of five steps: data collection, archetype development, physics-based parametric modeling, machine learning modeling, and urban building energy performance analysis. The devised methodology is tested on the Irish residential building stock and generates a synthetic building dataset of one million buildings through the parametric modeling of 19 identified vital variables for four residential building archetypes. As a part of the machine learning modeling process, the study implemented an end-use demand segregation method, including heating, li","cbCainHS4PaaMY6o","https://ap.wps.com/l/cbCainHS4PaaMY6o","pdf",2601241,1,17,"English","en",105,"# Abstract\n# Introduction\n## Background and policy context\n## Motivation for scalable prediction","[{\"question\":\"What problem does the paper address in urban building energy performance modeling?\",\"answer\":\"It addresses the mismatch between building energy modeling and traditional planning caused by inconsistent energy data and limited scalable building models, while large-scale surveys are time-consuming.\"},{\"question\":\"What method does the paper propose for predicting urban residential building energy performance?\",\"answer\":\"It proposes a data-driven machine learning approach using ensemble-based learning and end-use demand segregation, supported by a five-step workflow from data collection to urban-scale analysis.\"},{\"question\":\"How is model accuracy improved in the study?\",\"answer\":\"The study improves performance by using an ensemble-based machine learning approach, achieving 91% accuracy compared with 76% for the traditional approach.\"}]","Urban Building Energy Performance Prediction and Retrofit Analysis - Data-Driven Machine Learning Approach | PDF",1785720849,43,{"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},"urban-building-energy-performance-prediction-and-retrofit-analysis-data-driven-machine-learning-approach","",{"@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/urban-building-energy-performance-prediction-and-retrofit-analysis-data-driven-machine-learning-approach/118897/",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},"What problem does the paper address in urban building energy performance modeling?","Question",{"text":75,"@type":76},"It addresses the mismatch between building energy modeling and traditional planning caused by inconsistent energy data and limited scalable building models, while large-scale surveys are time-consuming.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What method does the paper propose for predicting urban residential building energy performance?",{"text":80,"@type":76},"It proposes a data-driven machine learning approach using ensemble-based learning and end-use demand segregation, supported by a five-step workflow from data collection to urban-scale analysis.",{"name":82,"@type":73,"acceptedAnswer":83},"How is model accuracy improved in the study?",{"text":84,"@type":76},"The study improves performance by using an ensemble-based machine learning approach, achieving 91% accuracy compared with 76% for the traditional approach.","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"]