Abstract
The previous chapter introduced the concept of OpenStudio Measures and how they can be applied individually and in combination to a Model to create and compare different Design Alternatives. While an improvement from modifying models by hand, generating results, and comparing them; the manual analysis workflow is still labor intensive, non-scalable, and will not necessarily yield the best solution for a given problem. In this chapter, we will discuss how OpenStudio enables automated creation and search of large building parameter spaces. We’ll also look at how these same approaches may be used to “tune” models of existing buildings to best match measured energy consumption data.
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Change history
12 August 2018
This book was inadvertently published without the online supplementary files for chapters 2 through 9. This has now been updated accordingly.
Notes
- 1.
- 2.
McKay et al. (1979).
- 3.
Morris (1991).
- 4.
Sobol (2001).
- 5.
Saltelli et al. (1999).
- 6.
Nelder and Mead (1965).
- 7.
Kennedy and Eberhart (1995).
- 8.
Barricelli (1957).
- 9.
Fraser (1957).
- 10.
Deb et al. (2002).
- 11.
Belding (1995).
- 12.
Zitzler and Thiele (1998).
- 13.
King et al. (2010).
- 14.
Mebane and Sekhon (2011).
- 15.
While sampling algorithms (e.g. LHS ) will not respect prescribed maximum and minimum values, they are used to constrain solutions generated by PAT’s optimizers .
- 16.
Amazon routinely updates its pricing structure. Prices listed in PAT are only estimates, and the user is ultimately responsible for knowing what costs may be incurred by an analysis .
- 17.
You may choose to use fewer samples for faster simulations or if you have fewer workers available. Our analysis will produce 6 × 75 or 450 data points.
- 18.
It is important to note that model calibration should always be performed with AMY data corresponding to the period that the available calibration data was obtained. Using TMY data to calibrate a model is like mixing apples and avocados. That said, our “utility billing data” was generated from TMY weather conditions, so it is appropriate to use that weather file in this exercise.
- 19.
You can manually copy the OSM into your PAT project’s seed sub-folder or you can use the file menu in PAT to select the revised model, but do not forget this step.
- 20.
You may choose to leave the OpenStudio Results output s in your project. We have removed them to minimize server reporting clutter.
- 21.
ASHRAE Guideline 14–2014: Measurement of Energy, Demand and Water Savings, ASHRAE, 2014.
- 22.
Algorithms like SPEA2, PSO, Rgenoud, etc. will make the most cost-effective use of your server and workers by setting the number of samples to be equal to the number of worker nodes available. This minimizes the number of workers that sit idle between optimizer iterations.
- 23.
Because of the dimensionality of the space the optimizer is traversing, we can only visualize slices of the solution in plots like these.
- 24.
Give some thought to definition of an objective function. For example, optimizing on annual energy use alone will result in a building with no windows, maximum insulation, ultra-high efficiency HVAC systems, etc. Add additional objectives (e.g. cost, comfort, or competing factors like heating and cooling energy) to create more realistic optimization problems.
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Brackney, L., Parker, A., Macumber, D., Benne, K. (2018). Parametric Analysis with OpenStudio. In: Building Energy Modeling with OpenStudio. Springer, Cham. https://doi.org/10.1007/978-3-319-77809-9_7
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