A Generalizable Method for Estimating Household Energy by Neighborhoods in US Urban Regions

Wenwen Zhang, Subhrajit Guhathakurta, Ram Pendyala, Venu Garikapati, Catherine Ross

Research output: Contribution to journalConference articlepeer-review

6 Scopus citations


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 languageEnglish (US)
Pages (from-to)859-864
Number of pages6
JournalEnergy Procedia
StatePublished - 2017
Externally publishedYes
Event1st Joint Conference on World Engineers Summit - Applied Energy Symposium and Forum: Low Carbon Cities and Urban Energy, WES-CUE 2017 - Singapore, Singapore
Duration: Jul 19 2017Jul 21 2017

All Science Journal Classification (ASJC) codes

  • General Energy


  • energy modeling
  • household synthesis
  • machine learning
  • residential energy
  • statistical matching


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