[Bldg-sim] deriving shorter timestep data from monthly or annual

Abushakra, Dr. Bass abushakr at msoe.edu
Mon Aug 31 09:50:52 PDT 2015


Joe,


In addition to what you noted, RP-1404 does show the user how the utility bills could be transformed to the hourly time scale, regressed, and used for hourly prediction for the whole year (or any shorter time frame).  This is shown in the final report with a lot of success (acceptable levels of uncertainty) to predict whole building electric use, and also thermal loads (hot water, and chilled water).


Thanks.


Bass


________________________________
From: Bldg-sim <bldg-sim-bounces at lists.onebuilding.org> on behalf of Joe Huang <yjhuang at whiteboxtechnologies.com>
Sent: Sunday, August 30, 2015 11:33 PM
To: bldg-sim at lists.onebuilding.org
Subject: Re: [Bldg-sim] deriving shorter timestep data from monthly or annual

Jeff, Chris,

I don't think RP-1404 sounds the same as what Chris is after.  In RP-1404, the goal is to find the shortest monitoring period, e.g., 2 weeks, from which long-term annual performance can be reliably extrapolated.   From Chris' post, I get the feeling that he was interested in ways to take utility bills and somehow disaggregate them by end use and  shorter time periods (daily or even hourly).

If all you're working with is monthly gas utility bills, I can't think of any other way than to regress them against variable-base degree days ala' the Princeton Scorekeeping Method (PRISM).  Use the base temperature with the best R2.  Then, take the average of the gas consumption for those months with no degree days as the non-weather-related gas consumption. The regression slope then allows you to apportion the weather-related gas consumption by day.

All this assumes that both the base load and the heating load/degree-day are constant, which may be rather dubious, especially for something like a stadium.

That's all for my two-cents.

Joe



Joe Huang
White Box Technologies, Inc.
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Moraga CA 94556
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On 8/27/2015 1:17 PM, Jeff Haberl wrote:

Hello Chris,



I think the definitive work on this was done as part of ASHRAE Research project 1404 by Bass Abushakra and Agami Reddy...(see below).



Jeff

1404-RP
Measurement, Modeling, Analysis and Reporting Protocols for Short-term M&V of Whole Building Energy Performance



Completed January 2014
Milwaukee School of Engineering
Principal Investigator, Bass Abushakra
TC 4.7, Energy Calculations

The objective of this research is to develop a new method to determine the shortest time period for energy use monitoring involving hourly (or sub-hourly) data that will yield reliable and accurate long term energy use estimates within acceptable uncertainty limits. By evaluating the uncertainty in the measured data as the monitoring period progresses, the new method will allow users to evaluate the energy performance and calculate energy savings in commercial and institutional buildings, in a cost-effective short-term monitoring period instead of the current year-long monitoring stipulated in most M&V protocols. The new approach would resolve the problem of needing long-term monitored data, which is often very costly to obtain and/or historically unavailable. In addition, this measurement/extrapolation approach should be designed as simply as possible to meet the uncertainty targets in energy savings stipulated in M&V protocols such as ASHRAE Guideline 14.


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Jeff S. Haberl, Ph.D.,P.E.inactive,FASHRAE,FIBPSA,......<mailto:........jhaberl at tamu.edu>jhaberl at tamu.edu<mailto:jhaberl at tamu.edu>
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________________________________
From: Bldg-sim [bldg-sim-bounces at lists.onebuilding.org<mailto:bldg-sim-bounces at lists.onebuilding.org>] on behalf of Chris Yates [chris.malcolm.yates at gmail.com<mailto:chris.malcolm.yates at gmail.com>]
Sent: Thursday, August 27, 2015 2:44 PM
To: bldg-sim at lists.onebuilding.org<mailto:bldg-sim at lists.onebuilding.org>
Subject: [Bldg-sim] deriving shorter timestep data from monthly or annual

Dear all,

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.

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.

In an ideal world, it would be helpful to develop the daily consumption weighted with the following factors:

  *   Parasitic losses - these can be estimated from when the facility is largely closed during the off season
  *   Historic game fixtures - readily available from the web
  *   Weather, including about 1MW of underpitch heating

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?

Many thanks

Chris



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