Stochastic Method for Generating Residential Household Energy Models of Varying Income Level and Climate Zone for Testing Energy Fairness

Abstract

Abstract Recent developments in residential energy management necessitate testing of whether energy technology and policy fairly impact households of various income levels and locations. Considering income level is particularly important because increased utility bills put low-income households at higher risk of economic and health issues. This paper proposes a novel method for generating diverse household energy models of varying income levels and climate zones, which can be used to test the fairness impacts of residential energy developments. Models are stochastically generated using probability distributions based on data from national surveys. Included in the model are constant and time-variable features. Models capture the randomness inherent in the residential sector while still following realistic patterns of each income-climate group regarding building structure, appliance stock, and occupant behavior. Models were validated by comparing when, how, and how much energy is consumed by simulated versus real-life surveyed households. A total of 200 household models were simulated for the validation process, representing four climate zones and five income levels. Simulated energy consumption was plotted against survey data from the same income level, climate zone, and income-climate combination. Through correlation analysis and null hypothesis testing, it was determined that there is no statistically significant difference between simulated and surveyed energy consumption. For all cases considered, the correlation of the data is highly statistically significant. When validating all cases for income-climate classifications and individual climate zones, there was less than a 0.05% chance of uncorrelated data exhibiting such high correlation coefficients (r2), which ranged from 0.862 to 0.998. When validating models of each income level, this chance ranged from less than 0.05% to 0.194% with r2 values ranging from 0.816 to 0.984. A linear trendline was fitted to the data, and a null hypothesis test was performed to check if the slope statistically differed from 1. All cases tested resulted in a P value greater than 0.05 which, for a 95% confidence level, indicates that no significant difference can be determined. Because the plot of simulated versus survey data was very highly correlated and exhibited a slope statistically indistinguishable from 1, simulated household models were determined to represent real-life households with sufficient accuracy.