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Towards The Removal Of Uncertainty In Sustainable Building Design Through Full Scale Optimization

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thesis
posted on 24.05.2021, 11:45 by Stuart C. Fix
The lack of whole-building design optimization resources available to building designers has led to uncertainty in design decisions involved with building highly sustainable or 'Green' buildings. This uncertainty can be removed using Full Scale Optimization: the process of conducting a massive number of building energy simulations, and combining this predicted operational data with life cycle analysis metrics to optimize building design. This method has been executed over the scope of 1 080 000 single detached home designs under Toronto climate conditions by automating EnergyPlus simulations within Amazon's Elastic Compute Cloud. A lifetime energy consumption analysis was performed using data from Athena's Impact Estimator. Example analysis shows parameters such as total building size, sub-grade floor area, window U-value, and air infiltration level have the greatest effect on total lifetime energy consumption. Future research is to include more rigorous database analysis and the inclusion of other relevant optimization metrics.

History

Language

eng

Degree

Master of Applied Science

Program

Building Science

Granting Institution

Ryerson University

LAC Thesis Type

Thesis