[Bldg-sim] Statistical method for data from limited parametric runs

[Jeff Haberl]

Hello Chris,



There is a method and it works like crazy if it is applied correctly to certain classes of buildings.



It’s called an "inverse method". However, applying it to houses in mass is not yet prime time. ASHRAE has some guidance on inverse methods for commercial buildings  in the soon-to-be-published Guideline-14 . When this comes out it will include a copy of the FORTRAN RP1050 toolkit and the spreadsheet RP1093 toolkit. With the RP1050 toolkit you can apply ASHRAE linear and change-point linear models to energy usage data and extract the gross parameters that the 3p change-point models yield. ASHRAE's RP1050 also provides a variable based degree day model, which is the same as PRISM, the Princeton Scorekeeping method. These gross parameters have been shown to be useful in determining weather-dependent and weather-independent parameters for a building. There are even papers where there has been speculation that the PRISM parameters could be "mined" to dig further into the "inverse description" of a building, such as Ari Rabl's 1980s paper published in Energy and Building’s special PRISM volume, and the more recent work by Kissock et al. at the University of Dayton on residential.



Ongoing work at our lab will soon show that these methods can be further extended for certain classes of buildings to approach an automated audit that “guesses” the most probably U-value and SHGC for a window, R-value for roofs, etc. So far we've had some success using this “blind calibration” method. We’re expecting a PhD thesis on this in the Spring 2014.



Jeff



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Jeff S. Haberl, Ph.D.,P.E.,FASHRAE,FIBPSA,........jhaberl at tamu.edu<mailto:........jhaberl at tamu.edu>
Professor............................................................Office Ph: 979-845-6507
Department of Architecture.............................Lab Ph:979-845-6065
Energy Systems Laboratory.............................FAX: 979-862-2457
Texas A&M University.....................................77843-3581
College Station, Texas, USA, 77843..................URL:www.esl.tamu.edu
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________________________________
From: bldg-sim-bounces at lists.onebuilding.org [bldg-sim-bounces at lists.onebuilding.org] on behalf of Chris Yates [chris.malcolm.yates at gmail.com]
Sent: Wednesday, October 30, 2013 6:48 AM
To: bldg-sim at lists.onebuilding.org
Subject: [Bldg-sim] Statistical method for data from limited parametric runs

Re-posted (Not sure if the original got on the list?)

Dear all,

I'm hoping to find a method that can be implemented in Excel for extracting a glazing transmittance from two or more sets of temperature results.

Our problem:
We assess simulated comfort criteria where operative temperatures in a zone above 27degC must be in the 1%-ile or less. We extract a glazing transmission that will 'only just' meet this criterion from two or more sets of results. We have limited access to parametric runs, hence only two or more sets of thermal modelling results.

What we've used up to now:
In order to 'roughly' evaluate the solar transmissivity that helps achieve this criterion we have used TREND and other functions for curve fitting in excel to interpolate a transmissivity from two or more sets of results.

We have two problems with this approach:
1. such a narrow band of results are being focused on so the calc is subject to inaccuracies
2. our modelling software is limited to one hour timesteps and can only report temperatures in 1 degree intervals

Possible solution?
My gut fee is that we can extract the information more reliably from a broader band of data. Is there an approach that looks at the spread of temperature distribution from, say, 25 to 28 degC for two or more result sets and extracts the transmissivity that matches the 1%-ile.

Is there such a method?

Minor note: We limit changes to glazing absorptance between our parametric runs, aiming for only changes in transmissivity and reflectance. We have found operative temperatures to increase with higher glazing absorptance.

Best regards

Chris

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