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Title: Estimating residential energy consumption in metropolitan areas: A microsimulation approach

Abstract

Prior research has shown that land use patterns and the spatial configurations of cities have a significant impact on residential energy demand. Given the pressing issues surrounding energy security and climate change, there is renewed interest in developing and retrofitting cities to make them more energy efficient. Yet deriving micro-scale residential energy footprints of metropolitan areas is challenging because high resolution data from energy providers is generally unavailable. In this study, a bottom-up model is proposed to estimate residential energy demand using datasets that are commonly available in the United States. The model applies novel machine learning methods to match records in the Residential Energy Consumption Survey with Public Use Microdata samples. This matching and machine learning produce a synthetic household energy distribution at a neighborhood scale. The model was tested and validated with data from the Atlanta metropolitan region to demonstrate its application and promise.

Authors:
; ; ; ; ; ;
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
OSTI Identifier:
1479263
Report Number(s):
NREL/JA-5400-72673
Journal ID: ISSN 0360-5442
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Journal Article
Journal Name:
Energy (Oxford)
Additional Journal Information:
Journal Volume: 155; Journal Issue: C; Journal ID: ISSN 0360-5442
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; residential energy; consumption; data synthesis; statistical matching; machine learning

Citation Formats

Zhang, Wenwen, Robinson, Caleb, Guhathakurta, Subhrajit, Garikapati, Venu M., Dilkina, Bistra, Brown, Marilyn A., and Pendyala, Ram M. Estimating residential energy consumption in metropolitan areas: A microsimulation approach. United States: N. p., 2018. Web. doi:10.1016/j.energy.2018.04.161.
Zhang, Wenwen, Robinson, Caleb, Guhathakurta, Subhrajit, Garikapati, Venu M., Dilkina, Bistra, Brown, Marilyn A., & Pendyala, Ram M. Estimating residential energy consumption in metropolitan areas: A microsimulation approach. United States. doi:10.1016/j.energy.2018.04.161.
Zhang, Wenwen, Robinson, Caleb, Guhathakurta, Subhrajit, Garikapati, Venu M., Dilkina, Bistra, Brown, Marilyn A., and Pendyala, Ram M. Sun . "Estimating residential energy consumption in metropolitan areas: A microsimulation approach". United States. doi:10.1016/j.energy.2018.04.161.
@article{osti_1479263,
title = {Estimating residential energy consumption in metropolitan areas: A microsimulation approach},
author = {Zhang, Wenwen and Robinson, Caleb and Guhathakurta, Subhrajit and Garikapati, Venu M. and Dilkina, Bistra and Brown, Marilyn A. and Pendyala, Ram M.},
abstractNote = {Prior research has shown that land use patterns and the spatial configurations of cities have a significant impact on residential energy demand. Given the pressing issues surrounding energy security and climate change, there is renewed interest in developing and retrofitting cities to make them more energy efficient. Yet deriving micro-scale residential energy footprints of metropolitan areas is challenging because high resolution data from energy providers is generally unavailable. In this study, a bottom-up model is proposed to estimate residential energy demand using datasets that are commonly available in the United States. The model applies novel machine learning methods to match records in the Residential Energy Consumption Survey with Public Use Microdata samples. This matching and machine learning produce a synthetic household energy distribution at a neighborhood scale. The model was tested and validated with data from the Atlanta metropolitan region to demonstrate its application and promise.},
doi = {10.1016/j.energy.2018.04.161},
journal = {Energy (Oxford)},
issn = {0360-5442},
number = C,
volume = 155,
place = {United States},
year = {2018},
month = {7}
}