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Results for “BESTEST-EX”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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13 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2011

Building Energy Simulation Test for Existing Homes (BESTEST-EX) Methodology: Preprint

The test suite represents a set of cases applying the new Building Energy Simulation Test for Existing Homes (BESTEST-EX) Methodology developed by NREL. (Judkoff et al. 2010a). The NREL team developed the test cases in consultation with the home retrofit industry (BESTEST-EX Working Group 2009), and adjusted the test specifications in accordance with information supplied by a participant withaccess to large utility bill datasets (Blasnik 2009).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2011

Building Energy Simulation Test for Existing Homes (BESTEST-EX) [Slides]

This presentation discusses the goals of NREL Analysis Accuracy R&D; BESTEST-EX goals; what BESTEST-EX is; how it works; 'Building Physics' cases; 'Building Physics' reference results; 'utility bill calibration' cases; limitations and potential future work.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2010

Building Energy Simulation Test for Existing Homes (BESTEST-EX); Phase 1 Test Procedure: Building Thermal Fabric Cases

The U.S. Department of Energy tasked NREL to develop a process for testing the reliability of models that predict retrofit energy savings, including their associated calibration methods. DOE asked NREL to conduct the work in phases so that a test procedure would be ready should DOE need it to meet legislative requirements related to residential retrofits in FY 2010. This report documents the initial 'Phase 1' test procedure.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2013

Evaluation of Automated Model Calibration Techniques for Residential Building Energy Simulation

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.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2013

Evaluation of Automated Model Calibration Techniques for Residential Building Energy Simulation

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.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗