Degree

Doctor of Philosophy (PhD)

Department

Construction Management

Document Type

Dissertation

Abstract

The built environment consumes a significant amount of energy in the United States, with outdated and inefficient affordable housing increasing financial pressure on low-income households (LIHs) who face high energy burdens. This research aims to assess the energy performance of American households, focusing on LIHs, to promote energy justice by providing data-driven insights and models for energy efficiency improvements. More specifically, this research aims to (1) create a large-scale national residential building energy dataset by integrating the American Housing Survey and the Residential Energy Consumption Survey using machine learning techniques; (2) analyze and model American household occupancy profiles by leveraging time-use surveys and applying machine learning algorithms to predict occupancy patterns based on influential sociodemographic factors; (3) assess the influence of different Energy Efficiency Measures (EEMs) on residential energy consumption across various household types, and (4) develop comprehensive models to support energy improvement decisions and policies at both the building and community levels, focusing on reducing energy burdens and promoting energy justice among LIHs. These contributions aim to advance sustainable and equitable energy use in the built environment, particularly benefiting LIHs facing higher energy burden.

Date

7-7-2025

Committee Chair

Jafari, Amirhosein

DOI

10.31390/gradschool_dissertations.6838

Available for download on Tuesday, July 07, 2026

Included in

Engineering Commons

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