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This study proposes a design-process framework identifying three phases where machine learning can optimize energy consumption during early work, focusing on Phase 1. Using limited inputs typical of business-as-usual workflows, the CatBoost model enables building-energy forecasting and improves design exploration. A New York City case study achieves high accuracy (R2=0.88) and uses Shapley additive explanation to validate influential parameters. Model-to-physics discrepancies of -8.69% to 11.04% are treated as acceptable for early-stage design, supporting sustainability-informed neighborhood decisions.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/leveraging-machine-learning-to-forecast-neighborhood-energy-use-in-early-design-stages-a-preliminary-application/290770/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/leveraging-machine-learning-to-forecast-neighborhood-energy-use-in-early-design-stages-a-preliminary-application/290770.png","ImageObject",300,407,{"name":92,"@type":93},"Rowan","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-18","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the study address in neighborhood-scale building design?","Question",{"text":112,"@type":113},"It targets the need to improve energy efficiency decisions earlier in the design process despite environmental pressures and increasing energy costs.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How is Phase 1 of the framework executed?",{"text":117,"@type":113},"Phase 1 applies machine learning to forecast building energy using only the few inputs available in a typical early-stage workflow.",{"name":119,"@type":110,"acceptedAnswer":120},"Why is the CatBoost model used, and what results were achieved?",{"text":121,"@type":113},"CatBoost is selected for high accuracy with minimal input data; the case study shows R2 scores of 0.88 for both cross-validation and test datasets.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},290770,1789642540,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":129,"read_time":26,"preview_page_count":4},1099514067415,"https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502","3.1  \n3.4  \nArticle  \nLeveraging Machine Learning to Forecast Neighborhood Energy Use in Early Design Stages: A Preliminary Application  \nAndrea Giuseppe di Stefano , Matteo Ruta, Gabriele Masera and Simi Hoque  \nSpecial Issue  \nData Analysis and Energy Modeling in Smart and Zero-Energy Buildings and Communities Edited by  \nDr. Marilena De Simone  \n[https://doi.org/10.3390/buildings14123866](https://doi.org/10.3390/buildings14123866)  \nArticle  \nLeveraging Machine Learning to Forecast Neighborhood Energy Use in Early Design Stages: A Preliminary Application  \nAndrea Giuseppe di Stefano 1, *, Matteo Ruta 1, Gabriele Masera 1 and Simi Hoque 2  \nCitation: di Stefano, A.G.; Ruta, M.; Masera, G.; Hoque, S. Leveraging Machine Learning to Forecast Neighborhood Energy Use in Early Design Stages: A Preliminary Application. Buildings 2024, 14, 3866 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)buildings14123866  \nAcademic Editor: Marilena De Simone  \nReceived: 22 October 2024  \nRevised: 19 November 2024  \nAccepted: 26 November 2024  \nPublished: 30 November 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Architecture, Built Environment and Construction Engineering, Politecnico di Milano, Via Ponzio 31, 20133 Milan, Italy; [matteo.ruta@polimi.it](matteo.ruta@polimi.it) (M.R.); [gabriele.masera@polimi.it](gabriele.masera@polimi.it) (G.M.)  \n2 Department of Civil, Architectural and Environmental Engineering, Drexel University, 3141 Chestnut Street, Philadelphia, PA 19104, USA; [sth55@drexel.edu](sth55@drexel.edu)  \n* Correspondence: [andreagiuseppe.distefano@polimi.it](andreagiuseppe.distefano@polimi.it)  \nAbstract: The need for energy ef􀀂ciency in neighborhood-scale architectural design is driven by environmental imperatives and escalating energy costs. This study identi􀀂es three key phases in a design process framework where machine learning can be applied to optimize energy consumption in early design stages. The overall framework integrates machine learning tools into the design work􀀃ow, enhancing design exploration from concept level and enabling targeted energy assessments. This paper focuses on the 􀀂rst phase (Phase 1) of the framework, which employs machine learning for building energy forecasting using only the few inputs available in a business-as-usual earlystage design work􀀃ow. The CatBoost model was selected for its high accuracy in predicting energy consumption using minimal input data. A preliminary application to a case study in New York City showed high predictive accuracy while reducing the input needed, with R2 scores of 0 .88 for both cross-validation and test datasets. Shapely additive explanation analysis validated the selection of key in􀀃uencing parameters such as building area, principal building activity, and climate zones. The test demonstrated discrepancies between the test data-driven model and a physics-based energy model values ranging from 􀀀8.69% to 11.04%, which can be considered an acceptable result in early-stage design. The remaining two phases, though outside the scope of this study, are introduced at a conceptual level to provide an overview of the full framework. Phase 2 will analyze building shape and elevation, assessing the total energy use intensity, while Phase 3 will apply district-level energy optimization across interconnected buildings. The 􀀂ndings from Phase 1 underscore the potential of machine learning to integrate energy ef􀀂ciency considerations into neighborhood-scale design from the earliest stages, providing reliable predictions that can inform sustainable design.  \nKeywords: predictive analysis; energy ef􀀂ciency strategie","cbCair7vWp2DqqDN","https://ap.wps.com/l/cbCair7vWp2DqqDN","pdf",2155809,24,"English","# Abstract\n# Keywords\n# 1. Introduction","[{\"question\":\"What problem does the study address in neighborhood-scale building design?\",\"answer\":\"It targets the need to improve energy efficiency decisions earlier in the design process despite environmental pressures and increasing energy costs.\"},{\"question\":\"How is Phase 1 of the framework executed?\",\"answer\":\"Phase 1 applies machine learning to forecast building energy using only the few inputs available in a typical early-stage workflow.\"},{\"question\":\"Why is the CatBoost model used, and what results were achieved?\",\"answer\":\"CatBoost is selected for high accuracy with minimal input data; the case study shows R2 scores of 0.88 for both cross-validation and test datasets.\"}]","Leveraging Machine Learning to Forecast Neighborhood Energy Use in Early Design Stages - A Preliminary Application | PDF"]