Principal component analysis of the electricity consumption in residential dwellings

Abstract

Data gathered from energy audits, phone surveys and smart meter readings are used to derive regression models of the electricity consumption of housing units in Oshawa (Ontario, Canada). The database used comprises 59 predictors, for 62 observations. To address the problem of multi-collinearities among the predictors and at the same time reduce the number of needed predictors, a methodology is developed based on the latent root regression technique of Hawkins [5]. Contrary to other variable selection techniques such as the stepwise method, the technique used in this paper allows an easy identification of alternative subsets. Using this technique, a reduction of 85% in the number of predictors is obtained, leaving only nine of them in the final subset. These nine variables are the number of occupants, the house status (owned or rented), the number of weeks of vacation per year, the type of fuel used in the pool heater, the type of fuel used in the heating system, the type of fuel used in the domestic hot water heater, the existence or not of an air conditioning system, the type of air conditioning system, and the number of air changes per hour at 50 Pa. A regression with these nine predictors leads to an R 2 of 0.79, with an adjusted R 2 of 0.75 and all regression coefficients statistically significant at the 95% confidence level.