Emerging computational methods to support the design and analysis of high performance buildings

dc.contributor.authorCant, Kevin
dc.contributor.supervisorEvins, Ralph
dc.date.accessioned2022-04-21T19:54:16Z
dc.date.available2022-04-21T19:54:16Z
dc.date.copyright2022en_US
dc.date.issued2022-04-21
dc.degree.departmentDepartment of Civil Engineeringen_US
dc.degree.levelMaster of Applied Science M.A.Sc.en_US
dc.description.abstractThis thesis presents three emerging computational methods: machine learning, gradient-free optimization, and Bayesian modelling. Each method is showcased in its ability to enable energy savings in new and existing buildings when paired with dynamic energy models. Machine learning algorithms provide rapid computational speed increases when used as surrogate models, supporting early-stage designs of buildings. Genetic algorithms support the design of complex interacting systems in a reduced amount of effort. Finally, Bayesian modelling can be leveraged to incorporate uncertainty in building energy model calibration. These methods are all readily available and user-friendly, and can be incorporated into current engineering workflows.en_US
dc.description.scholarlevelGraduateen_US
dc.identifier.bibliographicCitationRulff, D., Cant, K., and; Evins, R. (2021). Analysis of Feature Importance in Modeling Ground Source Heat Pump Systems Using Broad Parametric Analysis, Load Characterization and Artificial Neural Networks. In eSim 2021 Conference Proceedings. Vancouver, BC; IBPSAen_US
dc.identifier.bibliographicCitationCant, K., and; Evins, R. (2021). Optimizing VAV Terminal Box Minimum Positions using Dynamic Simulations to Improve Energy and Ventilation Performance. In Building Simulation 2021 Conference Proceedings. Bruges; IBPSAen_US
dc.identifier.bibliographicCitationCant, K., and; Evins, R. (2022). Improved Calibration of BuildingModels using Approximate Bayesian Calibration and Neural Networks. Submitted to the Journal of Building Performance Simulationen_US
dc.identifier.urihttp://hdl.handle.net/1828/13864
dc.languageEnglisheng
dc.language.isoenen_US
dc.rightsAvailable to the World Wide Weben_US
dc.subjectBuilding Energyen_US
dc.subjectEnergy Simulationen_US
dc.subjectMachine Learningen_US
dc.subjectBayesian Inferenceen_US
dc.titleEmerging computational methods to support the design and analysis of high performance buildingsen_US
dc.typeThesisen_US

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