A multilevel regression approach to understand effects of environment indicators and household features on residential energy consumption

Modeling residential energy consumption survey (RECS) data is a complex socio-technical problem that involves macroeconomics, climate, physical characteristics of housing, household demographics and usage of appliances. A multilevel regression (MR) model is introduced to calculate the magnitude and significance of effects of environment indicators and household features on residential energy consumption (REC). MR helps construct a conceptual framework and organize explanatory variables. The benefit of this approach is that based on stratified sampling schemes, MR extracts area effects from total variations of REC and explains the remaining variations with manifest variables and their interactions. Using the US 2009 RECS micro data consisting of 10,838 unique cases, 26 primary determinants of REC are found to be division groups, housing type, house size, usage of space heating equipment, household size and use of air-conditioning, etc. MR helps to quantify 82% of area effects and 47% of household effects. Proportion of the overall explained variance proportion is 53% compared to <40% using OLS regression models.