Multivariate regression as an energy assessment tool in early building design

摘要

This paper presents a new modeling approach to quantify building energy performance in early design stages. Building simulation models can accurately quantify building energy loads, but are not amenable to the early design stages when architects need an assessment tool that can provide rapid feedback based on changes to high level design parameters. We utilize EnergyPlus, an existing whole building energy simulation program, within a Monte Carlo framework to develop a multivariate linear regression model based on 27 building parameters relevant to the early design stages. Because energy performance is sensitive to building size, geometry, and location, we model a medium-sized, rectangular office building and perform the regression in four different cities-Miami, Winston-Salem, Albuquerque, and Minneapolis-each representing a different climate zone. With the exception of heating in Miami, all R2 values obtained from the multivariate regressions exceeded 96%, which indicates an excellent fit to the EnergyPlus simulation results. The analysis suggests that a linear regression model can serve as the basis for an effective decision support tool in place of energy simulation models during early design stages. In addition, we present standardized regression coefficients to quantify the sensitivity of heating, cooling, and total energy loads to building design parameters across the four climate zones. The standardized regression coefficients can be used directly by designers to target building design parameters in early design that drive energy performance.