[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125282-en":3,"doc-seo-125282-105":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":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},125282,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Enhancing Energy Efficiency in Residential Buildings at the Design Stage - Through Statistical and Machine Learning Models","Energy use dominance in buildings creates serious environmental impacts that undermine human well-being. Building energy efficiency is therefore a key lever for reducing total consumption. Predicting building energy use supports conservation efforts and better design decisions. Statistical and machine learning approaches are widely used for operational building energy prediction, yet early-stage design suitability remains underexplored. This research develops a back-to-front designer-oriented model using statistical and ML methods to enable target energy specification and delivers results in under five minutes.","ENHANCING ENERGY EFFICIENCY IN RESIDENTIAL BUILDINGS AT THE DESIGN STAGE THROUGH STATISTICAL AND MACHINE LEARNING MODELS  \nBy  \nRAZAK A. OLU-AJAYI  \nA thesis submitted to the University of Hertfordshire in partial fulfilment of the requirements for the degree of  \nDoctor of Philosophy  \nABSTRACT  \nThe high proportion of energy consumed in buildings has led to significant environmental problems that negatively impact human existence. It is noted that the construction of energyefficient buildings can help reduce the overall energy consumed in buildings. The prediction of building energy use is largely proclaimed to be a method for energy conservation and improved decision-making towards decreasing energy usage. Statistical and Machine Learning (ML) methods are recognised as highly effective for producing desired outcomes in prediction tasks. Consequently, ML has been extensively applied in studies focusing on the energy consumption of operational buildings. However, few studies explore the suitability of ML algorithms for predicting potential building energy consumption during the early design stage to facilitate the construction of more energy-efficient buildings.  \nThis research developed a back-to-front model for building designers, using statistical and machine learning algorithms. Embracing a positivist paradigm due to its objective stance, allows for a rigorous investigation and experimental analysis of hypotheses and objective evaluation of various models. This research includes evaluating different feature selection impacts on models for classification and regression tasks, assessing various statistical and AI tools across several criteria within the building energy research domain, and comprehensively reviewing studies on various factors influencing energy use in buildings, among other investigations and analysis.  \nA key finding is that Gradient Boosting (GB) is identified as the most effective model in terms of both accuracy and computational efficiency. Through extensive investigation and analysis, GB emerged as the optimal choice for building an energy prediction model.  \nA significant contribution of this research is the development of a back-to-front model that allows building designers to specify target energy consumption values. By inputting values for relevant parameters into the optimization model, designers can obtain optimal values or specifications for building features required to achieve the desired energy consumption outcomes. Remarkably, the model produces results in less than five minutes. This will essentially revolutionize energy assessment at the conceptual stage of building development sustainably.  \nThis research not only advances the theoretical understanding of building energy consumption prediction but also engenders practical tools for architects, engineers, and stakeholders to develop a more sustainable and efficient building. The integration of such a model using statistical and machine learning approaches into the design stage marks a significant step towards attaining environmentally friendly and economically viable buildings.  \nDEDICATION  \nWith utmost reverence and gratitude, this thesis is firstly dedicated to Almighty Allah, the Most Merciful and Compassionate, whose infinite blessings and guidance have illuminated my path and sustained me throughout this journey. Subsequently, to my beloved parents, Alhaji and Alhaja R.A Olu-Ajayi, your prayers, guidance, love, sacrifices and unwavering faith in me have been a beacon of strength and inspiration and I am forever grateful for your endless love and guidance.  \nTo my amazing wife, Mololuwa Temitope Olu-Ajayi, whose patience, understanding, and unwavering support have been a source of immense comfort and motivation. Thank you for always standing by my side and believing in me.  \nTo my lovely sisters, Mariam Olu-Ajayi, Zainab Olu-Ajayi, Bisoye Sonaike, and Ibironke Solabi, your constant encouragement and belief in my abilities have been a s","cbCaif7IRKx5JxWR","https://ap.wps.com/l/cbCaif7IRKx5JxWR","pdf",20979748,1,324,"English","en",105,"# Abstract\n# Dedication\n# Acknowledgement\n# Table of Contents\n# List of Tables","[{\"question\":\"Why is enhancing energy efficiency in residential buildings important?\",\"answer\":\"High energy consumption in buildings drives significant environmental problems. Improving energy efficiency helps reduce overall building energy use and supports sustainability.\"},{\"question\":\"What modeling approach does the research propose for early-stage design?\",\"answer\":\"The study develops a back-to-front model for building designers that uses statistical and machine learning algorithms to support energy-related decision-making during the conceptual design stage.\"},{\"question\":\"Which model is identified as most effective and why?\",\"answer\":\"Gradient Boosting (GB) is found to be the most effective for both accuracy and computational efficiency, making it the optimal choice in the evaluated models.\"}]","Enhancing Energy Efficiency in Residential Buildings at the Design Stage - Through Statistical and Machine Learning Models | PDF",1785897943,816,{"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},"enhancing-energy-efficiency-in-residential-buildings-at-the-design-stage-through-statistical-and-machine-learning-models","",{"@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/enhancing-energy-efficiency-in-residential-buildings-at-the-design-stage-through-statistical-and-machine-learning-models/125282/",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":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is enhancing energy efficiency in residential buildings important?","Question",{"text":75,"@type":76},"High energy consumption in buildings drives significant environmental problems. Improving energy efficiency helps reduce overall building energy use and supports sustainability.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What modeling approach does the research propose for early-stage design?",{"text":80,"@type":76},"The study develops a back-to-front model for building designers that uses statistical and machine learning algorithms to support energy-related decision-making during the conceptual design stage.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model is identified as most effective and why?",{"text":84,"@type":76},"Gradient Boosting (GB) is found to be the most effective for both accuracy and computational efficiency, making it the optimal choice in the evaluated models.","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"]