{"id":{"repo_id":"carleton","oai_identifier":"oai:carleton.scholaris.ca:20.500.14718/41820"},"canonical_url":"https://search.dev.ndltd.org/etd/carleton/oai:carleton.scholaris.ca:20.500.14718/41820","repository":{"repo_id":"carleton","name":"Carleton University","base_url":"https://carleton.scholaris.ca/server/oai/request"},"display":{"title":"Development and Demonstration of Surrogate Models to Predict Energy-Related Building Features from Heating and Cooling Load Signature","abstract":"This thesis introduces an innovative, efficient, and cost-effective approach for screening office buildings to identify the buildings with the most retrofit potential. Traditional auditing methodologies, including advanced inverse-based methods, often face constraints in terms of engineering cost, time, high fidelity operational data availability, and scalability. Utilizing inverse model-based machine learning, surrogate models for 12 mid to high-rise buildings were developed to predict energy-related features from widely available heating and cooling load data. The research explores two methodologies: 1) unsupervised learning with clustering to group energy-related features, and 2) supervised learning using artificial neural networks (ANNs) to predict these features. Both methods utilize simulated data and three-parameter change point models (3P CPMs) for efficient characterization of energy performance. The results show reasonable accuracy in predictions, with clustering addressing multicollinearity, offering a scalable alternative to traditional audits. 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Both methods utilize simulated data and three-parameter change point models (3P CPMs) for efficient characterization of energy performance. The results show reasonable accuracy in predictions, with clustering addressing multicollinearity, offering a scalable alternative to traditional audits. A user-friendly software tool illustrating the real-world application of the surrogate model is also introduced.","abstract_has_math":false,"creators":["Ferreira, Shane"],"institution":"Carleton University","degree_name":"Master of Applied Science (M.App.Sc.)","degree_level":"Master's","degree_discipline":"Engineering, Building","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023","date_published":"2023","updated_at":"2026-07-24T01:34:39Z","subjects":[],"languages":["en"],"rights":["Copyright © 2023 the author(s). 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