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

Judkoff, R.; Neymark, J.; Polly, Ben

doi:10.2172/1219607

Cited by 10 publications

(20 citation statements)

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“…The approach can demonstrate whether software predictions are "right, on average" and provide useful information about level of uncertainty in energy use predictions. Empirically-based testing augments highly detailed software-to-software testing (e.g., BESTEST-EX as described by Judkoff et al [2010]). …”

Section: Advantages Of Approachmentioning

confidence: 99%

Assessment of the U.S. Department of Energy's Home Energy Scoring Tool

Roberts

Merket

Polly

et al. 2012

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NOTICEThis report was prepared as an account of work sponsored by an agency of the United States government. Neither the United States government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States government or any agency thereof. 1 In acknowledging individuals and organizations we do not mean to imply their endorsement of the research results. Our intention is simply to acknowledge their contributions and thank them. iv Executive SummaryThe National Renewable Energy Laboratory (NREL) conducted a series of assessments of the U.S. Department of Energy's (DOE) proposed Home Energy Scoring Tool (HEST). The primary objective of this work was to assess the accuracy of HEST as it was being developed and to provide information useful to DOE program managers and HEST development team at Lawrence Berkeley National Laboratory.NREL assessed the accuracy of HEST from the version used for the Home Energy Score pilot, released January 26, 2011, through the April 27, 2012 release. With the exception of Appendix A, Historical Progression of HEST Accuracy, this report reflects assessment of the April 27, 2012 release of HEST. Comparison of Predicted Energy Uses to Measured Energy UsesPredictions of electricity and natural gas (NG) consumption were compared with weathernormalized utility billing data for a mixture of newer and older homes located in Oregon, Wisconsin, Minnesota, North Carolina, and Texas. 2 The 859 electricity comparisons and 500 NG comparisons yielded the following:• HEST underpredicted electricity use by a median of 1%.• HEST underpredicted NG use by a median of 10%.The primary objective of the Home Energy Score program is to issue a score to the homeowner. The Score ranges from 1 to 10, where a home scoring a 1 uses the most energy and a home scoring a 10 uses the least. For 52% of the homes in this sample, the predicted Home Energy Score is within ±1 point of a score calculated from measured energy use. 3 Comparison of Predicted Energy Uses to Predictions From Other ToolsSimilar comparisons were made between predictions from two other commonly used residential energy analysis software tools, REM/Rate and SIMPLE, and weather-normalized utility billing data for the same set of homes. The results of the comparisons are presented along with those from HEST in Table ES-1 and Table ES-2. HEST energy use predictions compare well with the other two energy analysis software tools....

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Section: Advantages Of Approachmentioning

confidence: 99%

Assessment of the U.S. Department of Energy's Home Energy Scoring Tool

Roberts

Merket

Polly

et al. 2012

Self Cite

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“…Further details about developing calibration scenarios are provided in Appendix F of Judkoff et al (2010a).…”

Section: Calibration Scenariosmentioning

confidence: 99%

“…This statistical approach was one of a number of possible methods, and the results are specific to the base-case house and the approximate input ranges defined in the field test. Further details of the analysis are provided in Appendix G of the final report (Judkoff et al 2010a). …”

Section: Benefit Of Calibrationmentioning

confidence: 99%

“…BESTEST-EX has been submitted for consideration to be included (and maintained) in ASHRAE Standard 140. Appendix I of the full final report (Judkoff et al 2010a Approximate input range: the specified range of possible values for an approximate input that forms the basis uncertainty range for selecting calibrated inputs for the tested programs, and from which explicit inputs are randomly selected. Also see calibrated input and explicit input.…”

Section: Future Workmentioning

confidence: 99%

“…Consequently, the National Renewable Energy Laboratory (NREL) began developing a process for testing the reliability of models that predict retrofit energy savings, including their associated calibration methods. NREL is conducting the work in phases; this paper summarizes the initial "Phase 1" test procedure and example acceptance criteria, which focus on building thermal fabric test cases (Judkoff et al 2010a(Judkoff et al , 2010b(Judkoff et al , 2011.…”

Section: Introductionmentioning

confidence: 99%

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

Judkoff¹,

Polly²,

Bianchi³

et al. 2011

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The authors developed a method for testing the reliability of models that predict retrofit energy savings, including their associated calibration methods. The test suite applies the new Building Energy Simulation Test for Existing Homes (BESTEST-EX) Methodology, developed by the National Renewable Energy Laboratory in consultation with home energy retrofit industry software developers. BESTEST-EX includes building physics test cases with fully known inputs, and calibrated energy savings test cases with specified base-case monthly utility billing data and uncertainty ranges for selected inputs. The test cases apply a variety of retrofit scenarios in a heating and a cooling climate.

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Reduced-order modeling and simulated annealing optimization for efficient residential building utility bill calibration

2015

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This simulation study applies the general framework described in BESTEST-EX for self-testing residential building energy model calibration methods. The National Renewable Energy Laboratory's BEopt/DOE-2.2 is used to evaluate an automated regression metamodeling-based calibration approach in the context of monthly 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, pseudo-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. A central composite design is used to develop response surface statistical models for the home's predicted energy use. Applying a gradient-based simulated annealing optimization algorithm to the statistical "metamodels", the calibration approach systematically adjusts values of the design variables and reduces disagreement between predicted energy use and synthetic utility billing data. Various retrofit measures are applied and used to assess accuracy of retrofit savings predictions resulting from using the calibration procedure. Substituting actual BEopt/DOE-2.2 model simulations with the statistical models reduces overall calibration procedure run-time while sacrificing only a limited degree of accuracy for retrofit savings predictions.

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

Cited by 10 publications

References 0 publications

Assessment of the U.S. Department of Energy's Home Energy Scoring Tool

Assessment of the U.S. Department of Energy's Home Energy Scoring Tool

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

Reduced-order modeling and simulated annealing optimization for efficient residential building utility bill calibration

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