To help solve the climate crisis, municipalities are increasingly modifying their
building codes and offering incentives to create greener buildings in their cities. But,
city planners find it difficult to set and assess these policies, as most municipalities
do not have the types of data used in urban building energy modelling (UBEM) that
would allow their planners to forecast the impacts of various building policies. This
thesis offers techniques for operating in this data-poor environment, presenting best
practices for developing data-driven archetypes with machine learning, demonstrating
inference of parameter values to improve archetypes by using surrogate modelling
and genetic algorithms, and a demonstration of techniques for assessing residential
retrofit impact in a data-limited environment, where data is neither detailed enough
to create an in-depth single archetype study, nor broad enough to create an UBEM
model.
It will be shown that inference techniques have potential, but need a certain amount
of detailed data to work, though far less than traditional UBEM techniques. For performing
residential retrofit, it will be shown the lack of ideal detailed data does not
present an overwhelming obstacle to drawing useful conclusions and that meaningful
insight can be extracted despite the lack of precision. Overall, this thesis shows a
data-poor environment, while challenging, is a viable environment for both research
and policy modelling. / Graduate
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/13679 |
| Date | 07 January 2022 |
| Creators | Therrien, Garrett E. S. |
| Contributors | Evins, R. |
| 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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