Modeling the Determinants of Residential Appliance Electricity Use Single-Family Homes, Homes with Electric Vehicles and Apartments

Abstract

This study provides a data mining-based methodology for setting decision-making rules to identify determinants of appliance electricity consumption based on four years of data from 800 single-family homes and apartments in Austin, Texas. These data were collected from single-family homes (i.e., reference case), single-family homes that owned electric vehicles (EV) and apartments containing multiple families. Cluster analysis was performed to group homes based on their calculated average hourly appliance electricity use, resident building attributes, and socioeconomic characteristics of building residents. Results of regression analysis indicate that variables from all three building types are significantly correlated to appliance electricity consumption. Residents of reference homes and single-family homes with EV tend to spend more time at homes, resulting in higher appliance consumption. Residents that own EVs generally attain a higher education level but do not necessarily having a lower consumption of appliances. Residents with higher income tend to have higher electricity consumption. The results of the analysis can provide new insights and tools for policymakers governing community development and for the utility sector as they seek to deploy new programs to optimize electricity use with existing generation capacity and enhance customer service in response to the growing demand for distributed generation in communities across America.