Change-point multivariable quantile regression to explore effect of weather variables on building energy consumption and estimate base temperature range

Abstract Mean regression analysis may not capture associations that occur primarily in the tails of the outcome distribution. In this study, we focused on building heating gas consumption related to multiple weather factors to find the extent to which they impact gas consumption at higher quantile levels. We used change-point multivariable quantile regression models to investigate distributional effects and heterogeneity in the gas consumption-related responses to weather factors. Subsequently, we analyzed quantile regression coefficients that corresponded to absolute differences in specific quantiles of gas consumption associated with a one-unit increase in weather factors. We found that the association of weather factors and gas consumption varied across 19 quantiles of gas consumption distribution. The heterogeneity of the case-study buildings was different: right tails of gas consumption for the community (CL) and educational (ED) buildings were more susceptible to weather factors than those of the health care (HL) building. The base temperature of the CL building across quantiles of gas consumption indicated a flat trend, but the uncertainty ranges were relatively large compared with those for the CL and ED buildings. This study could reveal which factors are most important and the extent to which they affect gas consumption at specific quantile levels.