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. / Graduate
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/13864 |
| Date | 21 April 2022 |
| Creators | Cant, Kevin |
| Contributors | Evins, Ralph |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
| Rights | Available to the World Wide Web |
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