1. Causal effects of policy and occupant behavior on cooling energy.
- Author
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Duhirwe, Patrick Nzivugira, Ngarambe, Jack, and Yun, Geun Young
- Subjects
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HOME energy use , *ENERGY policy , *CONSUMPTION (Economics) , *CAUSAL inference , *ARCHITECTURAL engineering - Abstract
Rising energy consumption in buildings has prompted architectural engineers and policymakers to ask "what-if" questions in the pursuit of energy efficiency. Current simulation and machine learning methods rely primarily on correlations and associations necessitating causal inference techniques. In this research, a causal inferential approach combining double machine learning and domain knowledge using directed acyclic graphs is employed. The utility of this approach is demonstrated on the 2015 United States Residential energy consumption survey to evaluate the impact of energy policies and occupant behavior on cooling energy consumption. The results show that energy policies such as energy audits, proper insulation, access to interval data and Energy Star qualified windows significantly reduce energy use intensity (EUI). Additionally, air conditioning usage by occupants causally affects EUI. For instance, proper insulation reduces EUI by 5.603 kWh/m2 while changing static thermostat settings to automatic adjustments at certain times reduces EUI by 3.542 kWh/m2. This study suggests potential energy-saving policies for balanced building energy efficiency and functionality. [Display omitted] • An ML-based causal inference analysis is proposed to explain cooling energy use. • The effects of energy policy and occupant behavior on energy use are quantified. • Significant policy improvements to enhance cooling energy use are proposed. • Occupant behaviors beneficial to cooling energy use are highlighted. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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