Analysis of Pre-Retrofit Building and Utility Data - Southeast United States
TO4 9.1.2: Comm. Scale Military Housing Upgrades
This project delves into the workflow and results of regression models on monthly and daily utility data (meter readings of electricity consumption), outlining a process for screening and gathering useful results from inverse models. Energy modeling predictions created in Building Energy Optimization software (BEopt) Version 2.0.0.3 (BEopt 2013) are used to infer causes of differences among similar homes. This simple data analysis is useful for the purposes of targeting audits and maximizing the accuracy of energy savings predictions with minimal costs.
The data for this project are from two adjacent military housing communities of 1,166 houses in the southeastern United States. One community was built in the 1970s, and the other was built in the mid-2000s. Both communities are all electric; the houses in the older community were retrofitted with ground source heat pumps in the early 1990s, and the newer community was built to an early version of ENERGY STAR with air source heat pumps. The houses in the older community will receive phased retrofits (approximately 10 per month) in the coming years. All houses have had daily electricity metering readings since early 2011.
This project explores a dataset at a simple level and describes applications of a utility data normalization. There are far more sophisticated ways to analyze a dataset of dynamic, high resolution data; however, this report focuses on simple processes to create big-picture overviews of building portfolios as an initial step in a community-scale analysis.
00_pre-retrofit - House Count: 36
01_pre-retrofit - House Count: 38
02_pre-retrofit - House Count: 27
03_pre-retrofit - House Count: 61
04_pre-retrofit - House Count: 196
05_pre-retrofit - House Count: 125
06_pre-retrofit - House Count: 62
Citation Formats
TY - DATA
AB - TO4 9.1.2: Comm. Scale Military Housing Upgrades
This project delves into the workflow and results of regression models on monthly and daily utility data (meter readings of electricity consumption), outlining a process for screening and gathering useful results from inverse models. Energy modeling predictions created in Building Energy Optimization software (BEopt) Version 2.0.0.3 (BEopt 2013) are used to infer causes of differences among similar homes. This simple data analysis is useful for the purposes of targeting audits and maximizing the accuracy of energy savings predictions with minimal costs.
The data for this project are from two adjacent military housing communities of 1,166 houses in the southeastern United States. One community was built in the 1970s, and the other was built in the mid-2000s. Both communities are all electric; the houses in the older community were retrofitted with ground source heat pumps in the early 1990s, and the newer community was built to an early version of ENERGY STAR with air source heat pumps. The houses in the older community will receive phased retrofits (approximately 10 per month) in the coming years. All houses have had daily electricity metering readings since early 2011.
This project explores a dataset at a simple level and describes applications of a utility data normalization. There are far more sophisticated ways to analyze a dataset of dynamic, high resolution data; however, this report focuses on simple processes to create big-picture overviews of building portfolios as an initial step in a community-scale analysis.
00_pre-retrofit - House Count: 36
01_pre-retrofit - House Count: 38
02_pre-retrofit - House Count: 27
03_pre-retrofit - House Count: 61
04_pre-retrofit - House Count: 196
05_pre-retrofit - House Count: 125
06_pre-retrofit - House Count: 62
AU - Beach, Robert
A2 - Prahl, Duncan
DB - Open Energy Data Initiative (OEDI)
DP - Open EI | National Renewable Energy Laboratory
DO -
KW - building america
KW - BuildingAmerica
KW - ASHRAE Guideline 14
KW - Inverse Modeling Toolkit
KW - data modeling
KW - energy modeling
KW - residential
KW - sliding regression
KW - weather normalization
KW - community
KW - existing home
KW - retrofit
KW - new construction
KW - HERS
KW - all climate
KW - single family
KW - multifamily
KW - very cold
KW - cold
KW - marine
KW - mixed humid
KW - hot humid
KW - hot dry
LA - English
DA - 2016/04/27
PY - 2016
PB - Ibacos Innovation
T1 - Analysis of Pre-Retrofit Building and Utility Data - Southeast United States
UR - https://data.openei.org/submissions/5239
ER -
Beach, Robert, and Duncan Prahl. Analysis of Pre-Retrofit Building and Utility Data - Southeast United States. Ibacos Innovation, 27 April, 2016, Open Energy Data Initiative (OEDI). https://data.openei.org/submissions/5239.
Beach, R., & Prahl, D. (2016). Analysis of Pre-Retrofit Building and Utility Data - Southeast United States. [Data set]. Open Energy Data Initiative (OEDI). Ibacos Innovation. https://data.openei.org/submissions/5239
Beach, Robert and Duncan Prahl. Analysis of Pre-Retrofit Building and Utility Data - Southeast United States. Ibacos Innovation, April, 27, 2016. Distributed by Open Energy Data Initiative (OEDI). https://data.openei.org/submissions/5239
@misc{OEDI_Dataset_5239,
title = {Analysis of Pre-Retrofit Building and Utility Data - Southeast United States},
author = {Beach, Robert and Prahl, Duncan},
abstractNote = {TO4 9.1.2: Comm. Scale Military Housing Upgrades
This project delves into the workflow and results of regression models on monthly and daily utility data (meter readings of electricity consumption), outlining a process for screening and gathering useful results from inverse models. Energy modeling predictions created in Building Energy Optimization software (BEopt) Version 2.0.0.3 (BEopt 2013) are used to infer causes of differences among similar homes. This simple data analysis is useful for the purposes of targeting audits and maximizing the accuracy of energy savings predictions with minimal costs.
The data for this project are from two adjacent military housing communities of 1,166 houses in the southeastern United States. One community was built in the 1970s, and the other was built in the mid-2000s. Both communities are all electric; the houses in the older community were retrofitted with ground source heat pumps in the early 1990s, and the newer community was built to an early version of ENERGY STAR with air source heat pumps. The houses in the older community will receive phased retrofits (approximately 10 per month) in the coming years. All houses have had daily electricity metering readings since early 2011.
This project explores a dataset at a simple level and describes applications of a utility data normalization. There are far more sophisticated ways to analyze a dataset of dynamic, high resolution data; however, this report focuses on simple processes to create big-picture overviews of building portfolios as an initial step in a community-scale analysis.
00_pre-retrofit - House Count: 36
01_pre-retrofit - House Count: 38
02_pre-retrofit - House Count: 27
03_pre-retrofit - House Count: 61
04_pre-retrofit - House Count: 196
05_pre-retrofit - House Count: 125
06_pre-retrofit - House Count: 62},
url = {https://data.openei.org/submissions/5239},
year = {2016},
howpublished = {Open Energy Data Initiative (OEDI), Ibacos Innovation, https://data.openei.org/submissions/5239},
note = {Accessed: 2025-04-23}
}
Details
Data from Apr 27, 2016
Last updated Aug 4, 2023
Submitted Apr 27, 2016
Organization
Ibacos Innovation
Contact
Robert Beach
Authors
Research Areas
Keywords
building america, BuildingAmerica, ASHRAE Guideline 14, Inverse Modeling Toolkit, data modeling, energy modeling, residential, sliding regression, weather normalization, community, existing home, retrofit, new construction, HERS, all climate, single family, multifamily, very cold, cold, marine, mixed humid, hot humid, hot dryDOE Project Details
Project Name Building America
Project Number 1.9.1.19
