[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125078-en":3,"doc-seo-125078-105":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},125078,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","The Applicability of a Machine Learning Methodology to Generate TMY Weather Files - A Master’s Thesis","Accurate building energy models are essential for decarbonization, and typical meteorological year (TMY) weather files are widely used to represent long-term climate conditions in energy simulations. This thesis investigates generating TMY files with machine learning to improve accuracy beyond an expert-judgment-based workflow that can miss seasonal, climate, and application-specific differences. Manuscript 1 develops a feature-importance-driven methodology to identify key Sandia generation parameters and improves long-term energy demand representativeness. Manuscript 2 evaluates applicability across six Canadian climate zones using standardized weighting factors, showing improved predictive performance and greater adaptability.","The Applicability of a Machine Learning Methodology to Generate TMY Weather Files  \nAshleigh Marie Papakyriakou  \nA Thesis  \nin  \nThe Department  \nOf  \nBuilding, Civil, and Environmental Engineering  \nPresented in Partial Fulfillment of the Requirements for the Degree of Master of Building Engineering at Concordia University  \nMontreal, Quebec, Canada  \nApril 2024  \n© Ashleigh Papakyriakou, 2024  \nCONCORDIA UNIVERSITY  \nSchool of Graduate Studies  \nThis is to certify that the thesis prepared  \nBy: Ashleigh Papakyriakou  \nEntitled: The Applicability of a Machine Learning Methodology to Generate TMY Weather Files  \nand submitted in partial fulfillment of the requirements for the degree of  \nMaster of Applied Science (Building Engineering)  \ncomplies with the regulations of the University and meets the accepted standards with respect to originality and quality.  \nSigned by the final Examining Committee:  \n  Chair  \nDr. Radu Zmeureanu  \n  Examiner  \nDr. Radu Zmeureanu  \n  Examiner  \nDr. Alireza Nazemi  \n  Supervisor  \nDr. Bruno Lee  \nApproved by   Chunjiang An, Graduate Program Director  \n  2024    \nDr. Mourad Debbabi, Dean of Faculty  \nAbstract  \nThe Applicability of a Machine Learning Methodology to Generate TMY Weather Files  \nAshleigh Papakyriakou  \nTo effectively decarbonize buildings accurate energy models must be created to predict building energy performance. Typical meteorological year (TMY) weather files represent long-term weather conditions and are used in energy modelling to help evaluate energy performance. This thesis explores generating TMY files using machine learning to improve accuracy, which can significantly influence energy simulation results. The current TMY generation approach relies on expert judgment, often overlooking seasonal, climate and application-based variations.  \nManuscript \\#1 introduces a machine learning methodology using feature importance to determine the relevant generation parameters used in the Sandia method to enhance the current TMY generation approach. The proposed methodology is applied to a medium office building in Montreal. The results reveal an improved representativeness of the long-term average building energy demand for the TMY generated using the proposed methodology.  \nManuscript \\#2 aims to (1) assess the applicability of the methodology across Canadian climates;  \n(2) investigate the feasibility of using standardized climate zone-based weighting factors to reduce the computational time associated with extracting location-based weighting factors to facilitate wider adoption of the proposed methodology. The methodology is applied to 18 cities across six Canadian climate zones and generates two weather files for each location. TMYSTATION uses location-based weighting factors while TMYCZ uses climate zone-based weighting factors. The CV(RMSE) and NMBE indicate the proposed weather files outperform the conventional weather files in predicting the long-term energy performance of buildings. Although the TMYSTATION files performed marginally better, the convenience of standardized climate zone-based weighting factors can enhance the methodology’s adaptability.  \nAcknowledgements  \nI would like to express my gratitude and appreciation to my supervisor Dr. Bruno Lee for his assistance and support. A special thank you goes to my dear friend, Ana, whose valuable insights, contributions, and dedicated time played a pivotal role in finalizing this contribution. Your friendship and support have meant the world to me. To my partner, Jared, and my close friends, and family, I am thankful for your support and patience during this challenging journey, and for encouraging me not to give up.  \nIn loving memory of my grandfather, John Papakyriakou, I dedicate this thesis. His enduring encouragement, guidance, kindness, and unwavering support has shaped me into the person I am today.  \nTable of Contents  \nLIST OF FIGURES.............................................................................","cbCaidIXdaF6ziKu","https://ap.wps.com/l/cbCaidIXdaF6ziKu","pdf",4484961,1,110,"English","en",105,"# Chapter 1 Introduction\n## Background\n## Literature Review\n## Objective\n## Thesis Structure\n## Manuscript Executive Summary","[{\"question\":\"Why are TMY weather files important for building energy modeling?\",\"answer\":\"TMY weather files represent long-term weather conditions and are used to predict building energy performance. Their accuracy can materially affect simulation results.\"},{\"question\":\"What does Manuscript #1 contribute to TMY generation?\",\"answer\":\"It introduces a machine learning methodology using feature importance to determine relevant generation parameters within the Sandia method, improving the representativeness of long-term average building energy demand.\"},{\"question\":\"How does Manuscript #2 expand the methodology for use across Canada?\",\"answer\":\"It assesses applicability across Canadian climates and tests standardized climate zone-based weighting factors to reduce computation time. Applied to 18 cities in six climate zones, the resulting files outperform conventional files in predicting long-term energy performance.\"}]","The Applicability of a Machine Learning Methodology to Generate TMY Weather Files - A Master’s Thesis | PDF",1785896509,277,{"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},"the-applicability-of-a-machine-learning-methodology-to-generate-tmy-weather-files-a-masters-thesis","",{"@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/the-applicability-of-a-machine-learning-methodology-to-generate-tmy-weather-files-a-masters-thesis/125078/",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","2026-08-05",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 TMY weather files important for building energy modeling?","Question",{"text":75,"@type":76},"TMY weather files represent long-term weather conditions and are used to predict building energy performance. Their accuracy can materially affect simulation results.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What does Manuscript #1 contribute to TMY generation?",{"text":80,"@type":76},"It introduces a machine learning methodology using feature importance to determine relevant generation parameters within the Sandia method, improving the representativeness of long-term average building energy demand.",{"name":82,"@type":73,"acceptedAnswer":83},"How does Manuscript #2 expand the methodology for use across Canada?",{"text":84,"@type":76},"It assesses applicability across Canadian climates and tests standardized climate zone-based weighting factors to reduce computation time. 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