Using surrogate models in the integrated design process for high-performance buildings

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In December 2016, the Pan-Canadian Framework on Clean Growth and Climate Change committed to meeting its international commitment of a 30% reduction of greenhouse gas (GHG) emissions below 2005 levels by 2030, of which 20 million tonnes are expected to come from the buildings sector.The technology and construction processes to make low carbon buildings are well understood; however, the buildings industry inherently requires front-loaded decision-making with long-term implications on building stock. The buildings constructed now are expected to still be present in 2030 and even 2050. Conventional analysis of building energy performance, carbon accounting and life-cycle costs through detailed simulation is time-consuming and expensive. In order to achieve the GHG reduction targets, tools must be developed that integrate effectively into the early concept screening and design decision-making processes, allowing rapid, reliable evaluation of options and measures.This project will adapt a machine learning method termed surrogate modeling to the building simulation domain, with an emphasis on breaking up the larger problem into sub-models (such as separating enclosure loads from HVAC delivery) that better align with a multi-discipline, integrated design process, as well as incorporating industry knowledge and producing tools for practical use by engineers, architects and planners during early concept design phases.Surrogate modeling relies on having a large number of validated baseline models to efficiently explore the design space for a new design (applying machine learning optimization techniques to identify where the new design fits among a constellation of pre-rendered models, without having to create a new high-fidelity model). The quality of the underlying baseline models is therefore vital, and this project will develop a validation methodology partly based on industry workflows for quality control. This will in turn support future work generating high-quality baselines for academic research, and in finding effective application in industry.This project will finally take the modules and methods described above and develop interactive tools for use by industry professionals. This will include research into effective visualizations and interface design, and will incorporate an iterative feedback process through phases of needs assessment surveys, workshops and field testing throughout the project timeline.