Bayesian processes for calibration of building performance simulation models and optimisation of building energy systems design

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Since the landmark Paris Agreement on Climate Change was struck in 2015, international organisations, national governments, and global industry have started new efforts at developing policy pathways, incentive schemes, and technology options for an immediate reduction in building-attributed greenhouse gas emissions. Particularly in the highly-regulated building markets of Europe, North America, and East Asia, redevelopments of building codes and standards have increasingly looked towards the use of building performance simulation (BPS) tools to facilitate decision-making and code-compliance for energy-efficient, low-carbon building design and retrofits. As this is ongoing, there remains two persistent barriers to the success of BPS-based decision-making in the real world: 1) the frequently-encountered performance gap represented by the discrepancy between expert-driven model predictions of building energy use and actual measurements, and 2) the lack of inexpensive and accessible techniques for BPS model calibration. The long-term objective of this project is to transform conventional building design practises by enabling the explicit representation of prediction uncertainty in building design problems that use BPS tools for decision-making. Specifically, the project proposes to research, develop, and validate: 1) a new approach to calibration of transient BPS models using Bayes theorem, 2) a new approach to stochastic optimisation of building design using Bayesian processes, and 3) a new approach to surrogate modelling of future building performance using Bayesian processes. To facilitate the project's long-term objective, the following short-term objectives will be delivered in sequence: (1) develop and execute an approach to assimilating building construction information and performance benchmarking data from at least 18 candidate commercial, residential, and institutional buildings in the Metro Vancouver region; (2) develop and execute an expert elicitation process to solicit any prior knowledge of uncertainty related to each building's construction information; (3) establish a best-practise approach to handling uncertainty within specific BPS sub-processes, such as occupancy prediction, building energy demand simulation, and energy supply systems modelling (4) systematically test and validate a new approach to Bayesian calibration of BPS models for each candidate building; (5) undertake an experimental investigation of the suitability of Bayesian BPS surrogate models to be applied for stochastic optimisation of building design and/or robust prediction of future building energy and environmental conditions. The overall outcome of the work will be the validation and dissemination of a new data-assisted simulation schema for predicting and optimising the energy consumption of buildings in Canada under prevailing technical and economic uncertainties.