- Argonne National Lab – Argonne, IL
- Thermal Energy Systems Specialists (TESS) – Madison, WI
- Joel Neymark Associates – Golden, CO
Performance Period: Oct. 1, 2019 – Sep. 30, 2022
Funding Source: 2019 Building Energy Modeling competitive lab call
Related Projects: Empirical Validation and Uncertainty Characterization, Empirical Validation of Energy Simulation: FLEXLAB, Empirical Validation of Energy Simulation: FRP, iUnit, and NZERTF, ASHRAE Standard 140, EnergyPlus.
Maximizing the adoption of high-performance buildings will require reducing the associated risks to clients and designers. A key part of reducing these risks is reducing the uncertainty, both real and perceived, in performance prediction. This project has the dual objectives of: (1) Improving the accuracy of simulation results by measuring and documenting validation data sets for use in identifying errors and inadequate assumptions in simulation engines so that they can be rectified by the developers, (2) characterizing the remaining uncertainty in simulation engines so that it may be properly controlled and accounted for in analyses and projects.
The project will build on the uncertainty framework devleoped by ANL in the Phase I Empirical Validation and Uncertainty Quantification project. It will analyze existing data and specs from designed-for-validation guarded twin cells, which support building thermal fabric physics not possible in other DOE facilities. This project will: determine where the existing data provides more accurate/conclusive results than other test-facility data, methodically build up test-cases (spec and data) and analysis from simplest to more complex, vet the test cases using both EnergyPlus and TRNSYS using independent modelers, and adapt the data and completed test specification for ASHRAE Standard 140 addendum.
The project team will work with the ASHRAE Standard 140 project committee to incorporate these test data sets into the standard. The team will also work with the EnergyPlus development team to identify and correct significant deficiencies.