Building design processes are dynamic and complex. The context of a building pro-
ject is manifold and depends on the cultural context, climatic conditions and personal
design preferences. Many stakeholders may be involved in deciding between a large
space of possible designs defined by a set of influential design parameters.
Building performance simulation is the state-of-the-art way to provide estimates of
the energy and environmental performance of various design alternatives. However,
setting up a simulation model can be labour intensive and evaluating it can be com-
putationally costly. As a consequence, building simulations often occur towards the
end of the design process instead of being an active component in design processes.
This observation and the growing availability of machine learning algorithms as an
aid to exploring analytical problems has lead to the development of surrogate mo-
dels. The idea of surrogate models is to learn from a high-fidelity counterpart, here
a building simulation model, by emulating the simulation outputs given the simula-
tion inputs. The key advantage is their computational efficiency. They can produce
performance estimates for hundreds of thousands of building designs within seconds.
This has great potential to innovate the field. Instead of only being able to assess
a few specific designs, entire regions of the design space can be explored, or instan-
taneous feedback on the sustainability of building can be given to architects during
design sessions.
This PhD thesis aims to advance the young field of building energy simulation
surrogate models. It contributes by: (a) deriving Bayesian surrogate models that are
aware of their uncertainties and can warn of large approximation errors; (b) deriving
surrogate models that can process large weather data (≈150’000 inputs) and estimate
the associated impact on building performance; (c) calibrating a simulation model via
fast iterations of surrogate models, and (d) benchmarking the use of surrogate-based
calibration against other approaches. / Graduate
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/12127 |
| Date | 14 September 2020 |
| Creators | Westermann, Paul W. |
| 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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