Abstract
There is mounting evidence to suggest that the urban built form plays a crucial role in household energy consumption, hence planning energy efficient cities requires thoughtful design at multiple scales - from buildings, to neighborhoods, to urban regions. While data on household energy use are essential for examining the energy implications of different built forms, few utilities providing power and gas offer such information at a granular scale. Therefore, researchers have used various estimation techniques to determine household and neighborhood scale energy use. In this study we develop a novel method for estimating household energy demand that can be applied to any urban region in the US with the help of publicly available data. To improve estimates of residential energy this paper describes a methodology that utilizes a matching algorithm to stitch together data from RECS with the Public Use Microdata Sample (PUMS) provided by the Bureau of Census. Our workflow statistically matches households in RECS and PUMS datasets based on the shared variables in both, so that total energy consumption in the RECS dataset can be mapped to the PUMS dataset. Following this mapping procedure, we generate synthetic households using processed PUMS data together with marginal totals from the American Community Survey (ACS) records. By aggregating energy consumptions of synthesized households, small area or neighborhood-based estimates of residential energy use can be obtained.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 859-864 |
| Number of pages | 6 |
| Journal | Energy Procedia |
| Volume | 143 |
| DOIs | |
| State | Published - Jan 1 2017 |
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Keywords
- energy modeling
- household synthesis
- machine learning
- residential energy
- statistical matching
ASJC Scopus subject areas
- Energy(all)
Cite this
A Generalizable Method for Estimating Household Energy by Neighborhoods in US Urban Regions. / Zhang, Wenwen; Guhathakurta, Subhrajit; Pendyala, Ram; Garikapati, Venu; Ross, Catherine.
In: Energy Procedia, Vol. 143, 01.01.2017, p. 859-864.Research output: Contribution to journal › Conference article
}
TY - JOUR
T1 - A Generalizable Method for Estimating Household Energy by Neighborhoods in US Urban Regions
AU - Zhang, Wenwen
AU - Guhathakurta, Subhrajit
AU - Pendyala, Ram
AU - Garikapati, Venu
AU - Ross, Catherine
PY - 2017/1/1
Y1 - 2017/1/1
N2 - There is mounting evidence to suggest that the urban built form plays a crucial role in household energy consumption, hence planning energy efficient cities requires thoughtful design at multiple scales - from buildings, to neighborhoods, to urban regions. While data on household energy use are essential for examining the energy implications of different built forms, few utilities providing power and gas offer such information at a granular scale. Therefore, researchers have used various estimation techniques to determine household and neighborhood scale energy use. In this study we develop a novel method for estimating household energy demand that can be applied to any urban region in the US with the help of publicly available data. To improve estimates of residential energy this paper describes a methodology that utilizes a matching algorithm to stitch together data from RECS with the Public Use Microdata Sample (PUMS) provided by the Bureau of Census. Our workflow statistically matches households in RECS and PUMS datasets based on the shared variables in both, so that total energy consumption in the RECS dataset can be mapped to the PUMS dataset. Following this mapping procedure, we generate synthetic households using processed PUMS data together with marginal totals from the American Community Survey (ACS) records. By aggregating energy consumptions of synthesized households, small area or neighborhood-based estimates of residential energy use can be obtained.
AB - There is mounting evidence to suggest that the urban built form plays a crucial role in household energy consumption, hence planning energy efficient cities requires thoughtful design at multiple scales - from buildings, to neighborhoods, to urban regions. While data on household energy use are essential for examining the energy implications of different built forms, few utilities providing power and gas offer such information at a granular scale. Therefore, researchers have used various estimation techniques to determine household and neighborhood scale energy use. In this study we develop a novel method for estimating household energy demand that can be applied to any urban region in the US with the help of publicly available data. To improve estimates of residential energy this paper describes a methodology that utilizes a matching algorithm to stitch together data from RECS with the Public Use Microdata Sample (PUMS) provided by the Bureau of Census. Our workflow statistically matches households in RECS and PUMS datasets based on the shared variables in both, so that total energy consumption in the RECS dataset can be mapped to the PUMS dataset. Following this mapping procedure, we generate synthetic households using processed PUMS data together with marginal totals from the American Community Survey (ACS) records. By aggregating energy consumptions of synthesized households, small area or neighborhood-based estimates of residential energy use can be obtained.
KW - energy modeling
KW - household synthesis
KW - machine learning
KW - residential energy
KW - statistical matching
UR - http://www.scopus.com/inward/record.url?scp=85040837016&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85040837016&partnerID=8YFLogxK
U2 - 10.1016/j.egypro.2017.12.774
DO - 10.1016/j.egypro.2017.12.774
M3 - Conference article
AN - SCOPUS:85040837016
VL - 143
SP - 859
EP - 864
JO - Energy Procedia
JF - Energy Procedia
SN - 1876-6102
ER -