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<p>Hello Chris,</p>
<p> </p>
<p>I guess I misread your question. Let me try again. </p>
<p> </p>
<p>Based on your original posting, I believe that you want to take monthly data and "break it down" into its components: weather dependent and weather-independent loads, as well as other useful "insights" provided by a regression analysis. Also, you were hoping
to get insight into daily or hourly use from monthly use. In general, ASHRAE IMT is highly recommended for analyzing weather dependent energy use data.</p>
<p> </p>
<p>As far as I know the original reference on this can be found in Robert Sonderegger's PhD thesis at Princeton in the later 1970s where he developed equivalent thermal parameters from the regressions he performed. Following this was the PRISM work performed
in the 1980s at Princeton, which was first summarized and published in the 1986 Energy and Buildings volume edited by Margaret Fels. In this volume are several articles that show the "lumped" properties of the three parameters contained in the PRISM model. </p>
<p> </p>
<p>If you use these parameters over time, and normalize them to your favorite variable, you can begin to develop insight into what's wrong with a building compared to similar buildings. In a sense this was the basis for the 1986 expert system thesis that I
developed at the University of Colorado, although I used a multi-parameter regression (i.e., Lotus) versus PRISM.
</p>
<p> </p>
<p>More recently, Professor Kelly Kissock at the University of Dayton has published much about the use of the parameters from PRISM or from a 3P or 4P change-point regression (i.e., ASHRAE's IMT, RP1050), to perform a "virtual audit", or preliminary assessment
of a residence before the auditor sets foot at the sight. His work includes both residential and industrial applications.</p>
<p> </p>
<p>This concept was carried one step further by Dr. Kee Han Kim (thesis available at the ESL), who successfully demonstrated that the parameters from a 3P regression can be used to diagnose problems with single-family residences in Texas using only monthly
utility bills and few other tidbits of information (i.e., year built, conditioned area, # of floors), in combination with simulation that is calibrated to the utility bills using the IMT and a set of rules.
</p>
<p> </p>
<p>Finally, a recent study by Paulus and Claridge has provided additional insight into an automated classification system for temperature-dependent model selection.</p>
<p> </p>
<p>Hope this helps.</p>
<p> </p>
<p>Jeff</p>
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<p> </p>
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<div><font color="#0000ff" size="2" face="Tahoma">8=! 8=) :=) 8=) ;=) 8=) 8=( 8=) 8=() 8=) 8=| 8=) :=') 8=) 8=?<br>
Jeff S. Haberl, Ph.D.,P.E.inactive,FASHRAE,FIBPSA,......<a href="mailto:........jhaberl@tamu.edu">jhaberl@tamu.edu</a><br>
Professor........................................................................Office Ph: 979-845-6507<br>
Department of Architecture............................................Lab Ph:979-845-6065<br>
Energy Systems Laboratory...........................................FAX: 979-862-2457<br>
Texas A&M University...................................................77843-3581<br>
College Station, Texas, USA, 77843.............................http://esl.tamu.edu<br>
8=/ 8=) :=) 8=) ;=) 8=) 8=() 8=) :=) 8=) 8=! 8=) 8=? 8=) </font><font color="#0000ff" size="2" face="Tahoma">8=0<br>
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<div id="divRpF321575" style="DIRECTION: ltr"><font color="#000000" size="2" face="Tahoma"><b>From:</b> Bldg-sim [bldg-sim-bounces@lists.onebuilding.org] on behalf of Chris Yates [chris.malcolm.yates@gmail.com]<br>
<b>Sent:</b> Thursday, August 27, 2015 2:44 PM<br>
<b>To:</b> bldg-sim@lists.onebuilding.org<br>
<b>Subject:</b> [Bldg-sim] deriving shorter timestep data from monthly or annual<br>
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<div>Dear all,<br>
<br>
I have monthly gas data for a soccer stadium and I'm trying to develop it toward daily (or even hourly) data so this can be used in subsequent assessments.<br>
<br>
My own efforts on previous assessments have extended to relatively crude application of multipliers (possibly based on a simulation study) and an overall divisor. This is very limited, especially in this case.<br>
<br>
In an ideal world, it would be helpful to develop the daily consumption weighted with the following factors:<br>
<ul>
<li>Parasitic losses - these can be estimated from when the facility is largely closed during the off season
</li><li>Historic game fixtures - readily available from the web </li><li>Weather, including about 1MW of underpitch heating </li></ul>
<p>I'm surrendered to the possibility that this is well beyond me! But, can anybody suggest excel functions or other software tools they may have used, or even what math for me to look up on Google?
<br>
</p>
<p>Many thanks <br>
</p>
<p>Chris<br>
</p>
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