[{"key":"dc.contributor.author","value":"Zhang, Haonan","language":null},{"key":"dc.date.accessioned","value":"2026-04-09T16:18:56Z","language":null},{"key":"dc.date.available","value":"2026-04-09T16:18:57Z","language":null},{"key":"dc.date.issued","value":"2026","language":"en"},{"key":"dc.identifier.uri","value":"http:\/\/hdl.handle.net\/2429\/93953","language":null},{"key":"dc.description.abstract","value":"Energy retrofits play a critical role in enhancing buildings\u2019 indoor thermal comfort, reducing\r\nenergy consumption, and mitigating greenhouse gas (GHG) emissions. However, identifying\r\noptimal retrofit strategies remains challenging due to the diverse building characteristics, occupant behaviours, and climate variability. Furthermore, conventional physics-based building energy modelling (BEM) used for energy retrofit evaluation often requires detailed building-specific\r\ninformation and involves complex modelling procedures, while the computational demand of\r\nphysics-based BEM poses additional limitations. This research aims to develop an integrated\r\napproach to evaluating building energy retrofit strategies by combining physics-based BEM with\r\ndata-driven approaches. A multi-stage approach was proposed to address key challenges and\r\nbridge existing research gaps.\r\nIn the first stage, a systematic literature review was conducted to examine current practices in\r\nenergy retrofit evaluation and identify uncertainty sources in BEM and retrofit assessment. In the\r\nsecond stage, a life cycle thinking-based energy retrofit evaluation framework was formulated.\r\nThis framework enables a comprehensive assessment of life cycle GHG emissions and life cycle\r\ncosts for various energy retrofit packages, facilitating holistic retrofit decision-making. The third\r\nstage introduced an integrated approach that combines physics-based BEM and interpretable\r\nmachine learning techniques to quantify uncertainties in retrofit evaluation and identify optimal\r\nenergy retrofit packages. This approach significantly improves the computational efficiency of\r\nconventional physics-based energy modelling and enhances the transparency of data-driven\r\ntechniques. In the fourth stage, a data-driven approach was developed to analyze post-retrofit\r\nbuilding energy load profiles and generate synthetic energy data using state-of-the-art deep\r\ngenerative models (DGMs). The results demonstrate that DGMs are effective in synthesizing fine-\r\ngrained energy data while addressing challenges related to data scarcity and privacy concerns.\r\nFinally, this research provided building energy modelling practices for energy retrofit practitioners\r\nand policy recommendations to promote the penetration of energy retrofit programs.\r\nThe outcomes of this research provide overall methodological and practical contributions to the\r\nfield of building energy research. The proposed approach supports multiple stakeholders, including energy researchers, retrofit practitioners, homeowners, utility providers, and municipalities, in evaluating retrofit impacts and identifying energy-efficient, cost-effective, and low-carbon retrofit strategies for existing residential buildings in Canada.","language":"en"},{"key":"dc.language.iso","value":"eng","language":"en"},{"key":"dc.publisher","value":"University of British Columbia","language":"en"},{"key":"dc.rights","value":"Attribution-NonCommercial-NoDerivatives 4.0 International","language":"*"},{"key":"dc.rights.uri","value":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/","language":"*"},{"key":"dc.title","value":"Evaluation of energy retrofits for residential buildings in Canada : an integrated modelling approach","language":"en"},{"key":"dc.type","value":"Text","language":"en"},{"key":"dc.degree.name","value":"Doctor of Philosophy - PhD","language":"en"},{"key":"dc.degree.discipline","value":"Civil Engineering","language":"en"},{"key":"dc.degree.grantor","value":"University of British Columbia","language":"en"},{"key":"dc.contributor.supervisor","value":"Hewage, Kasun","language":null},{"key":"dc.date.graduation","value":"2026-05","language":"en"},{"key":"dc.type.text","value":"Thesis\/Dissertation","language":"en"},{"key":"dc.description.affiliation","value":"Applied Science, Faculty of","language":"en"},{"key":"dc.description.affiliation","value":"Engineering, School of (Okanagan)","language":"en"},{"key":"dc.degree.campus","value":"UBCO","language":"en"},{"key":"dc.description.scholarlevel","value":"Graduate","language":"en"}]