Abstract
Representing Low-Income Households in Building Energy Modeling Tools
Highlights
In urban areas, low-income households experience a high energy burden and need to allocate a disproportionate share of their income to energy expenditures due to energy inefficiencies in their homes
Building energy performance modelling tools provide a basis for informative decision making. Such tools need occupancy related data to count for the effect of presence and activities of users on energy consumption and this need becomes challenging when little to no data is available for representing unique populations like low-income households
In this study population-specific data collected via a mail survey is used to refine the results of a Markov-chain probabilistic occupancy prediction model based on the American Time-Use Survey (ATUS)
Summary
Low-income households experience a high energy burden and need to allocate a disproportionate share of their income to energy expenditures due to energy inefficiencies in their homes. Representing Low-Income Households in Building Energy Modelling Tools Estimated U.S household energy costs as percentage of after‐tax income Left: Energy Burden (% of Income) Middle: Potential Electricity Savings (% of Baseline Consumption)
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Schedule a callMore From: SUS-RURI: Proceedings of a Workshop on Developing a Convergence Sustainable Urban Systems Agenda for Redesigning the Urban-Rural Interface along the Mississippi River Watershed held in Ames, Iowa, August 12–13, 2019
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