[Bldg-sim] deriving shorter timestep data from monthly or annual
Joe Huang
yjhuang at whiteboxtechnologies.com
Sun Aug 30 21:33:36 PDT 2015
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.
346 Rheem Blvd., Suite 205A
Moraga CA 94556
yjhuang at whiteboxtechnologies.com
http://weather.whiteboxtechnologies.com for simulation-ready weather data
http://www.whiteboxtechnologies.com
(o) (925)388-0265
(c) (510)928-2683
"building energy simulations at your fingertips"
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.
>
> 8=! 8=) :=) 8=) ;=) 8=) 8=( 8=) 8=() 8=) 8=| 8=) :=') 8=) 8=?
> Jeff S. Haberl,
> Ph.D.,P.E.inactive,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.............................http://esl.tamu.edu
> 8=/ 8=) :=) 8=) ;=) 8=) 8=() 8=) :=) 8=) 8=! 8=) 8=? 8=) 8=0
> ------------------------------------------------------------------------
> *From:* Bldg-sim [bldg-sim-bounces at lists.onebuilding.org] on behalf of
> Chris Yates [chris.malcolm.yates at gmail.com]
> *Sent:* Thursday, August 27, 2015 2:44 PM
> *To:* 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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