Westermann, Paul W.2020-09-152020-09-1520202020-09-14http://hdl.handle.net/1828/12127Building 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.enAvailable to the World Wide WebBuilding simulationMachine learningSurrogate modellingEnergy efficiencySustainable building designThesisSurrogate modelling for sustainable building design – A review, Energy and Buildings, Volume 198, 2019, Pages 170-186, ISSN 0378-7788, https://doi.org/10.1016/j.enbuild.2019.05.057. Paul Westermann, Matthias Welzel, Ralph Evins, Using a deep temporal convolutional network as a building energy surrogate model that spans multiple climate zones, Applied Energy, Volume 278, 2020, 115563, ISSN 0306-2619, https://doi.org/10.1016/j.apenergy.2020.115563. Paul Westermann, Chirag Deb, Arno Schlueter, Ralph Evins, Unsupervised learning of energy signatures to identify the heating system and building type using smart meter data, Applied Energy, Volume 264, 2020, 114715, ISSN 0306-2619, https://doi.org/10.1016/j.apenergy.2020.114715.