skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Evaluation of Automated Model Calibration Techniques for Residential Building Energy Simulation

Abstract

This simulation study adapts and applies the general framework described in BESTEST-EX (Judkoff et al 2010) for self-testing residential building energy model calibration methods. BEopt/DOE-2.2 is used to evaluate four mathematical calibration methods in the context of monthly, daily, and hourly synthetic utility data for a 1960's-era existing home in a cooling-dominated climate. The home's model inputs are assigned probability distributions representing uncertainty ranges, random selections are made from the uncertainty ranges to define 'explicit' input values, and synthetic utility billing data are generated using the explicit input values. The four calibration methods evaluated in this study are: an ASHRAE 1051-RP-based approach (Reddy and Maor 2006), a simplified simulated annealing optimization approach, a regression metamodeling optimization approach, and a simple output ratio calibration approach. The calibration methods are evaluated for monthly, daily, and hourly cases; various retrofit measures are applied to the calibrated models and the methods are evaluated based on the accuracy of predicted savings, computational cost, repeatability, automation, and ease of implementation.

Authors:
; ;
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy Building Technologies Program
OSTI Identifier:
1096687
Report Number(s):
NREL/TP-5500-60127
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; AUTOMATED MODEL CALIBRATION; NUMERICAL OPTIMIZATION; RESPONSE SURFACE METHODOLOGY; BESTEST-EX SELF-TESTING PROCEDURE; BEOPT/DOE-2.2; SIMULATED ANNEALING; ASHRAE 1051-RP; CENTRAL COMPOSITE DESIGN; BENEFIT OF CALIBRATION; RESIDENTIAL; RESIDENTIAL BUILDINGS; BUILDING AMERICA; NREL; Buildings

Citation Formats

Robertson, J., Polly, B., and Collis, J. Evaluation of Automated Model Calibration Techniques for Residential Building Energy Simulation. United States: N. p., 2013. Web. doi:10.2172/1096687.
Robertson, J., Polly, B., & Collis, J. Evaluation of Automated Model Calibration Techniques for Residential Building Energy Simulation. United States. https://doi.org/10.2172/1096687
Robertson, J., Polly, B., and Collis, J. 2013. "Evaluation of Automated Model Calibration Techniques for Residential Building Energy Simulation". United States. https://doi.org/10.2172/1096687. https://www.osti.gov/servlets/purl/1096687.
@article{osti_1096687,
title = {Evaluation of Automated Model Calibration Techniques for Residential Building Energy Simulation},
author = {Robertson, J. and Polly, B. and Collis, J.},
abstractNote = {This simulation study adapts and applies the general framework described in BESTEST-EX (Judkoff et al 2010) for self-testing residential building energy model calibration methods. BEopt/DOE-2.2 is used to evaluate four mathematical calibration methods in the context of monthly, daily, and hourly synthetic utility data for a 1960's-era existing home in a cooling-dominated climate. The home's model inputs are assigned probability distributions representing uncertainty ranges, random selections are made from the uncertainty ranges to define 'explicit' input values, and synthetic utility billing data are generated using the explicit input values. The four calibration methods evaluated in this study are: an ASHRAE 1051-RP-based approach (Reddy and Maor 2006), a simplified simulated annealing optimization approach, a regression metamodeling optimization approach, and a simple output ratio calibration approach. The calibration methods are evaluated for monthly, daily, and hourly cases; various retrofit measures are applied to the calibrated models and the methods are evaluated based on the accuracy of predicted savings, computational cost, repeatability, automation, and ease of implementation.},
doi = {10.2172/1096687},
url = {https://www.osti.gov/biblio/1096687}, journal = {},
number = ,
volume = ,
place = {United States},
year = {Sun Sep 01 00:00:00 EDT 2013},
month = {Sun Sep 01 00:00:00 EDT 2013}
}