All:
On a related note – Thanks to Brian Fountain, a very intriguing (free) piece of energy simulation software called Sim-EB (available here: https://www.simeb.ca/) which focuses on modeling existing commercial buildings recently came to my attention. While quite powerful, the Sim-EB software provides a number of unique features that I think are appropriate to this discussion:
1) Given ‘monthly’ utility consumption and demand, and user-defined input parameter constraints, Sim-EB provides an interface for supporting ‘auto-calibration’ of DOE2.1e models.
2) Given user defined building descriptions of elements common across the engines, (form, fabric, HVAC, controls, etc), provides a means for using either DOE2.1e, DOE2.2 or EnergyPlus to perform calculaitons
3) Allows for easy import of user edited .inp files or .idf into the Sim-EB software. These files could have been created elsewhere, and just use Sim-EB for calibration or analysis, results review, etc.
4) Has a user website (https://www.simeb.ca:8443/index_fr.jsp,) which automatically creates and emails to users a user defined hourly ‘AMY’ model files (for Quebec related cities) to support model calibration.
5) Give a flexible, uniform data series (short term data logger data, 15 minute energy data, etc.), Sim-EB provides a flexible clustering tool for observing ‘day-type load profiles’ from within a larger data set. One can cluster model output as well, as Sim-EB provides a good visualization tool for comparing (day-type load profile) clusters of model output to clusters of metered data.
#2) lets people see, even for a shoebox model, the differences in the loads/systems/plants between DOE2.1, DOE2.2 and EPlus calculation methodologies. Of course, there are a great deal of ‘switches’ between the three different simulation engines that the software automatically sets, but one can export the input files and study the switch settings if they need to.
#5) above supports a case of real field data informing model input, i.e. the generation of hourly schedules given actual, real data!
,
This software was created and is distributed by the good folks at Hydro Quebec. As such, it supports both English and Metric units and the menu trees can be switched between French and English (but the help is in French only). I’ve use the Google translator tool and the example files to learn how to use the tool successfully. There are great pdf reports distributed along with the software which describe the clustering algorithms, the auto-calibration routines, etc.
Finally – if one renames the Sim-Eb .swdf file format weather files with a .zip extension, you can see how to replace them with non-Canadian files. It’s a bit of a pain, but possible.
While it is certainly no panacea, I personally think this tool is a demonstration of how simulation software could be used to ‘marry’ simulation input/output with field data collection to provide ‘better’ calibrated simulations for examining retrofit potential. Recognizing the ‘perfect is not the enemy of the good’, this software, IMHO, forms a good compromise towards using energy models to create actionable results.
All the Best,
_Chris
Chris Balbach, PE, CEM, CMVP, BEAP, BESA, BEMP
Vice President of Research and Development
Performance Systems Development of NY, LLC
124 Brindley Street, Suite 4,Ithaca, NY 14850
ph: (607)-327-1647
From: bldg-sim-bounces@xxxxxxxxxxxxxxxxxxxxx [mailto:bldg-sim-bounces@xxxxxxxxxxxxxxxxxxxxx]
On Behalf Of Joe Huang
Sent: Thursday, June 28, 2012 4:14 PM
To: EnergyPlus_Support@xxxxxxxxxxxxxxx
Cc: bldg-sim@xxxxxxxxxxxxxxxxxxxxx
Subject: Re: [Bldg-sim] [EnergyPlus_Support] Food for thought....
Jim,
Sorry if I came off as a little smug. You've actually put in words the main reason why I think building energy modelers have steered away from using historical weather data, which is simply that of convenience, i.e., there are a lot of "typical year" weather
files floating around, but getting hold of
a usable historical year weather file takes a little more work.
For the study that I mentioned, Dru and I did not have to do anything because the SANSOM data set,
available from NCDC, contained 25 years of historical weather files for 239 cities (same as TMY2s).
That was in 1996, and the availability of historical weather data has only increased exponentially since
then, although the building energy modeling community seems curiously to not have followed.
The most notable change, in my view, is NCDC's decision to put the entire ISH (Integrated Surface Hourly) data online in 2005, and then to make it free to all in 2011. This means that the raw weather reports
from major stations around the world ( ~ 1,500 in the US) are available stretching back to 1980.
I've been working with the ISH for many years now, and am able to generate complete weather files from any ISH file within seconds, and have been providing that as a service to customers. Sometime later this
year, I'll be rolling out something on the Web, but for now, those interested can just send me an e-mail.
The idea that the ISH is of questionable quality is, in my view, rather backwards. The ISH is a repository of the weather reports by the "official" weather stations around the world, so if you can't trust that, what can you trust?
Joe
Joe Huang
White Box Technologies, Inc.
346 Rheem Blvd., Suite 108D
Moraga CA 94556
yjhuang@xxxxxxxxxxxxxxxxxxxxxxxx
www.whiteboxtechnologies.com
(o) (925)388-0265
(c) (510)928-2683
"building energy simulations at your fingertips"
On 6/28/2012 11:48 AM, Jim Dirkes wrote:
Dear Joe,
No fair! You and Dru have been at the forefront energy modeling research for most of my adult life, and have a big head start.
My guess is that you spent a lot of time preparing the actual weather files for the research, however. Unless I’m missing something, the ready availability of high quality (e.g., no big hunks of missing data) actual weather data has been pretty limited until recently. With folk like Weather Analytics getting on board and making it pretty easy to get and inexpensive, it becomes a lot faster and lower cost than trying to clean some of the NOAA / NCDC data, not to mention getting good data for sites not in or near a major city.
Kudos for being way ahead of the industry curve (at least my own curve)! It’s getting easier to catch up!
p.s., Dru sent me that paper and I’ll be reading it with interest very soon.
From: Joe Huang [mailto:yjhuang@xxxxxxxxxxxxxxxxxxxxxxxx]
Sent: Thursday, June 28, 2012 1:53 PM
To: EnergyPlus_Support@xxxxxxxxxxxxxxx
Cc: Jim Dirkes; bldg-sim@xxxxxxxxxxxxxxxxxxxxx
Subject: Re: [EnergyPlus_Support] Food for thought....
I've always thought it was a "no-brainer" to use actual weather data whenever you're comparing simulation results to actual consumption data. Even with the earliest degree-day software such as
PRISM (Princeton Scorekeeping Method) in the 1980's, it was stressed to use the degree days
from the period of record, and not the long-term average, so I'm not sure why this (using actual
year weather data) is such a revelation.
The variation in total energy consumption of course depends a lot on the building characteristics.
Back in 1996, Dru Crawley and I wrote a paper on "Does it matter which weather data you use in energy simulations?", for the ACEEE Summer Study on Energy Efficiency in Buildings (it also appeared as two
separate ASHRAE papers at around the same time) where we took some prototypical building models (Dru did commercial, I did residential) and ran them with various "typical year" weather files and also 25 years of historical data in 10-12 US locations.
Joe
Joe Huang
White Box Technologies, Inc.
346 Rheem Blvd., Suite 108D
Moraga CA 94556
yjhuang@xxxxxxxxxxxxxxxxxxxxxxxx
www.whiteboxtechnologies.com
(o) (925)388-0265
(c) (510)928-2683
"building energy simulations at your fingertips"
On 6/28/2012 8:49 AM, Jim Dirkes wrote:
Dear Forums,
I am busy preparing a short talk for the Fall ASHRAE Energy Modeling Conference. The topic is “An Approach for Calibrating Existing Building Energy Models to their Utility Consumption”.
As part of the preparation, I will address the issue of how muc h difference might result in energy conservation measure savings predictions if you use actual weather data for the billing period versus TMY data.
To get a rough idea how much variation there might be, I looked at Degree Days for a span of years. What a variation! (for the city I’m studying at least)
I am not yet sure how that affects total energy consumption – you’ll have to attend my presentation in Atlanta to find out J.
In the meantime, I am starting to think that existing building energy models should use actual weather, not TMY data. Have any of you run similar comparisons for existing building models?
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