Emerging computational methods to support the design and analysis of high performance buildings

Date

2022-04-21

Authors

Cant, Kevin

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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.

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Keywords

Building Energy, Energy Simulation, Machine Learning, Bayesian Inference

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