Covington, H.
, Woo-Shem, B.
, Wang, C.
, Roth, T.
, Nguyen, C.
and Lee, H.
(2023),
Stochastic Method for Generating Residential Household Energy Models of Varying Income Level and Climate Zone for Testing Energy Fairness, 17th International Conference on Energy Sustainability, Washington DC, MD, US, [online], https://doi.org/10.1115/ES2023-107421, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=936697
(Accessed December 13, 2024)
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
Secure .gov websites use HTTPS
A lock (
) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.
Stochastic Method for Generating Residential Household Energy Models of Varying Income Level and Climate Zone for Testing Energy Fairness
Published
Author(s)
Hannah Covington, Brian Woo-Shem, , , Cuong Nguyen, Hohyun Lee
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).Proceedings Title
17th International Conference on Energy Sustainability
Conference Dates
July 10-12, 2023
Conference Location
Washington DC, MD, US
Pub Type
Conferences
Download Paper
Citation
Issues
If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.
Created September 26, 2023, Updated October 2, 2023