TY - GEN
T1 - Empirical Validation of UBEM
T2 - 2024 ASHRAE Winter Conference
AU - Garg, Ankur
AU - Correa, Santiago
AU - Li, Fengqi
AU - Chowdhury, Shovan
AU - New, Joshua R.
AU - Bacabac, Kevin
AU - Kunkel, Christian
AU - Baird, Donnel
N1 - Publisher Copyright:
© 2024 ASHRAE.
PY - 2024
Y1 - 2024
N2 - Residential and commercial buildings currently account for 30% of total global final energy consumption. Urban-scale building energy modeling (UBEM) can enable scalable investments and unlock building improvements by quantifying energy, demand, emissions, and cost reductions of specific measures or packages for building-specific technologies in large geographic regions. While the sophistication of UBEM data sources and technologies have increased dramatically in the past decade, there remains a knowledge gap for empirical validation and sources of bias between building-specific energy models and measured data at varying geographic scales. As UBEM continues to develop, systemic analysis of accuracy, bias, and limitations of the resulting models is necessary to inform best practices and move toward standardization. These are characterized for the Automatic Building Energy Modeling (AutoBEM) software suite with an initial case study involving metered electricity consumption data from 247,188 buildings in Chicago, Illinois, USA - averaged across years 2019-2021 - compared to the following datasets: (1) the AutoBEM-generated nation-scale Model America version 2 (MAv2) data for 596,064 buildings, (2) tax assessor data for 579,829 buildings, (3) tax assessor data filled with MAv2, and (4) 102 representative dynamic archetypes. Accuracy is reported for every building type and vintage combination, along with multiple sources of bias for unique building descriptors. The AutoBEM simulation workflow produced energy consumption estimates that closely match aggregated metered electricity consumption data for different types of buildings constructed during various time periods at the city scale - with initial normalized mean bias error of 10.9% and 1.1% after removing outliers. The contribution of statistically significant factors, including building type, land use, age, and size, to variance in UBEM bias is quantified.
AB - Residential and commercial buildings currently account for 30% of total global final energy consumption. Urban-scale building energy modeling (UBEM) can enable scalable investments and unlock building improvements by quantifying energy, demand, emissions, and cost reductions of specific measures or packages for building-specific technologies in large geographic regions. While the sophistication of UBEM data sources and technologies have increased dramatically in the past decade, there remains a knowledge gap for empirical validation and sources of bias between building-specific energy models and measured data at varying geographic scales. As UBEM continues to develop, systemic analysis of accuracy, bias, and limitations of the resulting models is necessary to inform best practices and move toward standardization. These are characterized for the Automatic Building Energy Modeling (AutoBEM) software suite with an initial case study involving metered electricity consumption data from 247,188 buildings in Chicago, Illinois, USA - averaged across years 2019-2021 - compared to the following datasets: (1) the AutoBEM-generated nation-scale Model America version 2 (MAv2) data for 596,064 buildings, (2) tax assessor data for 579,829 buildings, (3) tax assessor data filled with MAv2, and (4) 102 representative dynamic archetypes. Accuracy is reported for every building type and vintage combination, along with multiple sources of bias for unique building descriptors. The AutoBEM simulation workflow produced energy consumption estimates that closely match aggregated metered electricity consumption data for different types of buildings constructed during various time periods at the city scale - with initial normalized mean bias error of 10.9% and 1.1% after removing outliers. The contribution of statistically significant factors, including building type, land use, age, and size, to variance in UBEM bias is quantified.
UR - http://www.scopus.com/inward/record.url?scp=85198974444&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85198974444
T3 - ASHRAE Transactions
SP - 1034
EP - 1043
BT - ASHRAE Winter Conference
PB - American Society of Heating Refrigerating and Air-Conditioning Engineers
Y2 - 20 January 2024 through 24 January 2024
ER -