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A new approach to performance-based building design exploration using linear inverse modeling

Despite the development of a large number of building performance simulation tools, designers still need a systematic framework appropriate for energy-oriented decision-making in the early stages of design. While the current workflow follows a “forward” modelling procedure in which simulation tools predict the performance of a design, this study proposes an “inverse” procedure that entails a performance objective that estimates design parameters. Using linear inverse modelling, this approach generates plausible ranges for design parameters given a preferred thermal performance. The paper begins by demonstrating that thermal demand in a particular building operation-and-climate condition can be expressed as a linear regression model and then, in two case-studies, uses the regression model to develop an inverse algorithm. After defining energy performance targets as input, users obtain a probabilistic estimate of design parameters as output that represents a large “menu” of feasible design solutions, provides confidence, and embodies the iterative nature of design.