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Title: Streamlining building energy efficiency assessment through integration of uncertainty analysis and full scale energy simulations

Abstract

Asset Score is a standardized rating system and tool for assessing a building's energy-related systems in the United States. The web-based tool models building energy use under standard operating conditions to rate the energy efficiency of the as-built building systems and enable level comparisons of building assets. With basic characteristics entered by users, the tool creates simplified EnergyPlus building models to support the rating analysis. However, even with a reduced set of model inputs, data collection remains a challenge and the commercial building market demands a more simplified entry point to the rating system. This paper discusses a hybrid method that combines regression models with real-time simulations to allow users to enter as few as seven building parameters to quickly assess the building energy performance prior to a full-scale analysis. Built upon large-scale building stock simulations, a random forest approach was used to develop a set of regression models for various building use types. The majority of the Asset Score tool inputs were sampled extensively and fed into the regression models. These were combined with inferred inputs and user-defined uncertainty levels to create a distribution of possible energy use intensities for the building and its Preview score. With additional usermore » inputs, the regression model can be transferred to an energy model for a full-scale energy simulation. The streamlined Asset Score Preview assessment provides an easy entry point to a full Asset Score assessment. It also enables users who manage a large number of buildings to screen and prioritize buildings that can benefit most from a more detailed evaluation and possible energy efficiency upgrades without intensive data collection.« less

Authors:
; ; ORCiD logo; ; ; ORCiD logo
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office
OSTI Identifier:
1465650
Report Number(s):
NREL/JA-5500-72220
Journal ID: ISSN 0378-7788
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Journal Article
Journal Name:
Energy and Buildings
Additional Journal Information:
Journal Volume: 176; Journal Issue: C; Journal ID: ISSN 0378-7788
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; basic characteristics; building energy efficiency; building energy performance; commercial building; energy use intensities; large scale buildings; real time simulations; standard operating conditions

Citation Formats

Goel, Supriya, Horsey, Henry, Wang, Na, Gonzalez, Juan, Long, Nicholas, and Fleming, Katherine. Streamlining building energy efficiency assessment through integration of uncertainty analysis and full scale energy simulations. United States: N. p., 2018. Web. doi:10.1016/j.enbuild.2018.06.041.
Goel, Supriya, Horsey, Henry, Wang, Na, Gonzalez, Juan, Long, Nicholas, & Fleming, Katherine. Streamlining building energy efficiency assessment through integration of uncertainty analysis and full scale energy simulations. United States. https://doi.org/10.1016/j.enbuild.2018.06.041
Goel, Supriya, Horsey, Henry, Wang, Na, Gonzalez, Juan, Long, Nicholas, and Fleming, Katherine. 2018. "Streamlining building energy efficiency assessment through integration of uncertainty analysis and full scale energy simulations". United States. https://doi.org/10.1016/j.enbuild.2018.06.041.
@article{osti_1465650,
title = {Streamlining building energy efficiency assessment through integration of uncertainty analysis and full scale energy simulations},
author = {Goel, Supriya and Horsey, Henry and Wang, Na and Gonzalez, Juan and Long, Nicholas and Fleming, Katherine},
abstractNote = {Asset Score is a standardized rating system and tool for assessing a building's energy-related systems in the United States. The web-based tool models building energy use under standard operating conditions to rate the energy efficiency of the as-built building systems and enable level comparisons of building assets. With basic characteristics entered by users, the tool creates simplified EnergyPlus building models to support the rating analysis. However, even with a reduced set of model inputs, data collection remains a challenge and the commercial building market demands a more simplified entry point to the rating system. This paper discusses a hybrid method that combines regression models with real-time simulations to allow users to enter as few as seven building parameters to quickly assess the building energy performance prior to a full-scale analysis. Built upon large-scale building stock simulations, a random forest approach was used to develop a set of regression models for various building use types. The majority of the Asset Score tool inputs were sampled extensively and fed into the regression models. These were combined with inferred inputs and user-defined uncertainty levels to create a distribution of possible energy use intensities for the building and its Preview score. With additional user inputs, the regression model can be transferred to an energy model for a full-scale energy simulation. The streamlined Asset Score Preview assessment provides an easy entry point to a full Asset Score assessment. It also enables users who manage a large number of buildings to screen and prioritize buildings that can benefit most from a more detailed evaluation and possible energy efficiency upgrades without intensive data collection.},
doi = {10.1016/j.enbuild.2018.06.041},
url = {https://www.osti.gov/biblio/1465650}, journal = {Energy and Buildings},
issn = {0378-7788},
number = C,
volume = 176,
place = {United States},
year = {2018},
month = {10}
}