Analyzing the Impact of Residential Building Attributes, Demographic and Behavioral Factors on Natural Gas Usage

This analysis examines the relationship between energy demand and residential building attributes, demographic characteristics, and behavioral variables using the U.S. Department of Energy’s Residential Energy Consumption Survey 2005 microdata. This study investigates the applicability of the smooth backfitting estimator to statistical analysis of residential energy consumption via nonparametric regression. The methodology utilized in the study extends nonparametric additive regression via local linear smooth backfitting to categorical variables. The conventional methods used for analyzing residential energy consumption are econometric modeling and engineering simulations. This study suggests an econometric approach that can be utilized in combination with simulation results. A common weakness of previously used econometric models is a very high likelihood that any suggested parametric relationships will be misspecified. Nonparametric modeling does not have this drawback. Its flexibility allows for uncovering more complex relationships between energy use and the explanatory variables than can possibly be achieved by parametric models. Traditionally, building simulation models overestimated the effects of energy efficiency measures when compared to actual "as-built" observed savings. While focusing on technical efficiency, they do not account for behavioral or market effects. The magnitude of behavioral or market effects may have a substantial influence on the final energy savings resulting from implementation of various energy conservation measures and programs. Moreover, variability in behavioral aspects and user characteristics appears to have a significant impact on total energy consumption. Inaccurate estimates of energy consumption and potential savings also impact investment decisions. The existing modeling literature, whether it relies on parametric specifications or engineering simulation, does not accommodate inclusion of a behavioral component. This study attempts to bridge that gap by analyzing behavioral data and investigate the applicability of additive nonparametric regression to this task. This study evaluates the impact of 31 regressors on residential natural gas usage. The regressors include weather, economic variables, demographic and behavioral characteristics, and building attributes related to energy use. In general, most of the regression results were in line with previous engineering and economic studies in this area. There were, however, some counterintuitive results, particularly with regard to thermostat controls and behaviors. There are a number of possible reasons for these counterintuitive results including the inability to control for regional climate variability due to the data sanitization (to prevent identification of respondents), inaccurate data caused by to self-reporting, and the fact that not all relevant behavioral variables were included in the data set, so we were not able to control for them in the study. The results of this analysis could be used as an in-sample prediction for approximating energy demand of a residential building whose characteristics are described by the regressors in this analysis, but a certain combination of their particular values does not exist in the real world. In addition, this study has potential applications for benefit-cost analysis of residential upgrades and retrofits under a fixed budget, because the results of this study contain information on how natural gas consumption might change once a particular characteristic or attribute is altered. Finally, the results of this study can help establish a relationship between natural gas consumption and changes in behavior of occupants