Emerging computational methods to support the design and analysis of high performance buildings
| dc.contributor.author | Cant, Kevin | |
| dc.contributor.supervisor | Evins, Ralph | |
| dc.date.accessioned | 2022-04-21T19:54:16Z | |
| dc.date.available | 2022-04-21T19:54:16Z | |
| dc.date.copyright | 2022 | en_US |
| dc.date.issued | 2022-04-21 | |
| dc.degree.department | Department of Civil Engineering | |
| dc.degree.level | Master of Applied Science M.A.Sc. | en_US |
| dc.description.abstract | This 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.scholarlevel | Graduate | en_US |
| dc.identifier.bibliographicCitation | Rulff, 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; IBPSA | en_US |
| dc.identifier.bibliographicCitation | Cant, 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; IBPSA | en_US |
| dc.identifier.bibliographicCitation | Cant, K., and; Evins, R. (2022). Improved Calibration of BuildingModels using Approximate Bayesian Calibration and Neural Networks. Submitted to the Journal of Building Performance Simulation | en_US |
| dc.identifier.uri | http://hdl.handle.net/1828/13864 | |
| dc.language | English | eng |
| dc.language.iso | en | en_US |
| dc.rights | Available to the World Wide Web | en_US |
| dc.subject | Building Energy | en_US |
| dc.subject | Energy Simulation | en_US |
| dc.subject | Machine Learning | en_US |
| dc.subject | Bayesian Inference | en_US |
| dc.title | Emerging computational methods to support the design and analysis of high performance buildings | en_US |
| dc.type | Thesis | en_US |