Abstract
Interest in residential energy management necessitates fairness testing of energy technology and policy developments. 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 level and climate zone, which can be used to test the fairness 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 in building structure, appliance stock, and occupant behavior. Validation of the model generation technique was done by comparing the energy consumption of simulated households to their counterparts in survey data. Models were validated with correlation analysis and t-tests, with the null hypothesis being that the slope found in correlation analysis was equal to 1. The null hypothesis could not be rejected (P > 0.05), and the data for all validation tests was very highly correlated (0.81 < r2 < 1.00).
