100 simulations

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I am just finishing up a project that performed about 60
automated simulations (using Python with EnergyPlus and
Eppy) for a series of buildings in a bunch of cities. The
power of automating simulations to understand the energy
savings of different measures is very impressive no matter
what tools are being used. It has made me wonder about when
does automation make the most sense during the design
process and what information can be provided to an architect
or entire building design team to encourage low energy
building design. I am thinking one of the most influential
times might be during the architectural programming and
early conceptual design steps. At this point the number of
separate pieces of information is probably low enough that
it could be filled out on a web form:

- number of occupants

- amount of area needed for different types of spaces

- location of the lot lines

- building location

Conceivably, with that information, all sorts of various
building configurations could be created automatically by a
clever script then simulated and the resulting answers
summarized.

- How many floor building uses the least energy?

- What shape building uses the least energy?

- What is the impact of more roof insulation?

- What is the impact of more or less fenestration on loads
and daylighting?

I would not expect the design team to use any of the
automatically created building models directly but it might
influence the design process in a good way if it was easy to
get and easy to understand. I understand people have been
researching the optimization of these kinds of factors but I
am not sure that is necessary. Maybe just several different
series of simulations illustrating various building options
and their impact onenergy might be enough to get the
discussion going.

- So what questions do you think could be answered by such
an automated system during early conceptual design?

- How would you best convey that information to the
building design team?

- Are there other times that a suite of automated
simulations would make sense?

A lot of useful information could be generated with a
hundred automated simulations!

Jason

--
Jason Glazer, P.E., GARD Analytics, 90.1 ECB chair
Admin for onebuilding.org building performance mailing lists

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Jason,

This is something I'm interested in as well. I think all the preliminary
design factors that you mentioned are great things to look at. On the
later parts of the design process, control parameters are also good things
to look at (CHW plant control optimization, air-side control optimization,
etc.).

One way to convey the information is through simple 2D plots. Below is an
example of 200 DOE2 simulations while varying the window to wall ratio and
another plot of 81 simulations varying the window shading coefficient.

[image: Inline image 1][image: Inline image 2]

Unfortunately, this does not convey the interactive nature of optimization
over multiple variables. Using multidimensional optimization algorithms
can be another useful tool, but they can be tricky. As an example, below
is a case of looking for the optimal minimum condenser water flow in a
variable flow condenser system. From looking at the first plot, the
function seems relatively smooth and it's obvious that there's an optimal
in the neighborhood of 0.6. However, if you zoom in (second plot), you can
see that the data is not very smooth, and there are all kinds of jagged
local minima/maxima. These will tend to throw off most optimization
algorithms, which is why I think it's helpful to consider looking at
automated mass simulations before taking on the problem of optimization.

[image: Inline image 5]

[image: Inline image 4]
Aaron

Aaron Powers2's picture
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Joined: 2011-09-30
Reputation: 0

Aaron and Jason, I like this thread. I noticed that the curve Aaron
presented for WWR vs. Source Energy shows only a 4% difference in Source
Energy over a range of 0-50% WWR---I would say a negligible difference from
optimizing this component alone. An architect would shrug this off, despite
the thought and computing power that went into it. This got me to thinking
that, as an architect, the aspect my colleagues most have trouble with is
the arrangement of building elements, rather than the optimization of each
one.

Jason's questions related to arrangement---how many floors? what shape
building?---would be the most useful for conceptual design, in my opinion.
I would leave alone insulation levels and even WWR at the
programming/conceptual stage, and instead run hundreds of simulations with
different *arrangements* of zones. I would further clarify this point by
adding to Jason's questions list:

1. Does the conference room go on the east or west (re: sun exposure)?
Under the roof, or in the basement?
2. How deep can floor plates be, and still achieve adequate daylighting?
3. What aspect ratio of the atrium vs. the floor plates gives us adequate
natural ventilation?
4. If I put the fume hoods in the classroom, I have to ventilate the whole
classroom, but if I put them only in prep rooms, that is a much smaller air
volume...what difference does this make?
5. If I cluster all my public circulation to one side, and naturally
ventilate this, how much energy do I save vs. distributed circulation that
is conditioned with fans?
6. Add a giant, beautiful glass staircase---do I save enough elevator
energy to offset the conditioning of the staircase?

In typical optimization modeling, the geometry is fixed and we vary the
component parameters. I'd like to see the opposite.

If the auto-generating algorithm could produce a 3D mass diagram of
color-coded zone blobs, perhaps as a Sketchup object, that would
communicate well.

Again, architects tend to have trouble with the spatial arrangement of
zones as something driven by performance. Perhaps the most useful thing at
the programming/concept design stage is help with arrangement. Thank you,
Dan J

Dan Johnson | Design and Energy | 510.325.5672
Assoc. AIA, ASHRAE, LEED AP, CEPE, CPHC | 907 Ramona Ave. Albany California
94706

Dan Johnson's picture
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Joined: 2015-09-11
Reputation: 0

Dan,
You are right that most of the 'optimization' happens once all the
massing/shape/room locations/configurations are finalized. Even now, I
dread when I am asked to help with energy analysis during the early design
phase since I have this at the back of the mind that the architect might
come back with several totally new designs (shape/floors etc.) and I will
have to redo the whole process. This has never happened in my case, and
most decisions are WWR, insulation, glass types, shading and major system
types.
To get back to your question, for the type of analysis that you have
outlined, you might want to look at the expert system based research in
architecture departments where they work on automatic design generation.
Carnegie Mellon comes to mind since I was involved in this type of work
ages back (that was in the 90's.). Microstation/Autocad might have these
kind of tools inbuilt by now (I havn't really kept up with this topic), and
since they also have energy tools connection - what you are suggesting
seems doable.
-Rohini

R B's picture
R B
Offline
Joined: 2011-09-30
Reputation: 4

Hi Jason and Aaron

We've been working on this subject for a year or two, and we are confident that automated building simulations augmented by uncertainty analysis (UA) and sensitivity analysis (SA) have significant potential for the industry. In our research, we perform Monte Carlo simulations using a normative ISO 13790 model to investigate a vast, global design space in relation to energy consumption, thermal comfort, and daylight availability.

The iterative work flow is as follows:

1. Assign probability distributions to a wide range of design parameters believed to impact performance

2. Create sample matrix from distributions

3. Run Monte Carlo simulations (1000?s)

4. Perform UA, SA and multivariate analysis

During the early design we learn about:

- SA helps identify the most influential inputs at the particular stage in the design process. The design team may then focus on these design parameters having the largest impact on the output

- UA identifies best case and worst case scenarios under the given design variability

- Using Monte Carlo filtering the designer may apply filters (criteria/constraints) on one or more outputs to show which favorable regions of the design space are most likely to produce acceptable results

- If the simulationist has explored a sufficiently large design space, hundreds of the simulations will meet the requirements. In that case, Monte Carlo filtering may also be used for inputs to see the consequences of different design limits/choices. This is useful during workshops and meetings where the different actors are present (building owner, architects, engineers, contractors).

In a late, detailed design situation the designer may apply uncertainties to user behavior, time schedule, weather, etc. This will reveal the following:

- Again, SA identifies the inputs having most input on output variability. The designer can then try to gain more knowledge about these inputs to reduce variance of the output

- UA give more reliable results about the expected performance of the building. This may help reduce performance gaps.

We will present some of our work at the international IBPSA conference in India in December (?A stochastic and holistic method to support decision-making in early building design?).

P.S.

Preliminary screening methods, such as Morris Method (or method of elementary effects), may be performed to ?identify factors in the model which, left free to vary over their range of uncertainty, make no significant contribution to the variance of the output? [1]. This will

P.P.S.

Note about optimisation:

In the early stages no optimisation is performed since this may conflict with the interest of other stakeholders (building owners, architects, engineers, contractors). This may also conflict with qualitative measures such as aesthetics, function, constructions, etc.

In late design, optimisation is highly relevant to optimize on building controls, HVAC system, etc.

Best regards,

Torben ?sterg?rd

Industrial Ph.D. student at Aalborg University

MSc. Architectural Engineering at MOE A/S

[1] A. Saltelli, et al. (2008), Global sensitivity analysis: the primer. Wiley & Sons

________________________________
Fra: Aaron Powers [caaronpowers at gmail.com]
Sendt: 10. september 2015 19:46
Til: Jason Glazer
Cc: bldg-sim at onebuilding.org
Emne: Re: [Bldg-sim] 100 simulations

Jason,

This is something I'm interested in as well. I think all the preliminary design factors that you mentioned are great things to look at. On the later parts of the design process, control parameters are also good things to look at (CHW plant control optimization, air-side control optimization, etc.).

One way to convey the information is through simple 2D plots. Below is an example of 200 DOE2 simulations while varying the window to wall ratio and another plot of 81 simulations varying the window shading coefficient.

[Inline image 1][Inline image 2]

Unfortunately, this does not convey the interactive nature of optimization over multiple variables. Using multidimensional optimization algorithms can be another useful tool, but they can be tricky. As an example, below is a case of looking for the optimal minimum condenser water flow in a variable flow condenser system. From looking at the first plot, the function seems relatively smooth and it's obvious that there's an optimal in the neighborhood of 0.6. However, if you zoom in (second plot), you can see that the data is not very smooth, and there are all kinds of jagged local minima/maxima. These will tend to throw off most optimization algorithms, which is why I think it's helpful to consider looking at automated mass simulations before taking on the problem of optimization.

[Inline image 5]

[Inline image 4]
Aaron

=?Windows-1252?Q?Torben_=D8stergaard?='s picture
Joined: 2015-09-12
Reputation: 0

Parametric study of building variations is perhaps where computer simulations are at their
best in playing the what-if game!

It's troubling and unfortunate that there was more such studies back 20-30 years ago than
now, where the focus has been on developing ever more complicated building models and ever
more detailed algorithms, with the apparent goal of creating pristine one-time-use
building models.

I remember when I was first introduced to building energy simulations at UC Berkeley in
1980, one of my class assignments was to use Murray Milne's SOLAR-5 program to study the
change in loads for different building orientations. When I first went to LBNL (LBL in
those days :-)), I created a residential building energy data base for which I created 5
prototypical residential building models full of macros, which allowed me to do a hundred
parametric for each building model in 45 locations, stepping through common variations of
ceiling and wall insulation, window area/orientation/panes of glass, infiltration rates,
etc., or around 10,000-20,000 runs in all. These were then reduced to nonlinear equations
that drove simplified programs such as PEAR (Program for Energy Analysis of Residences,
LBNL 1987) or ARES (Automated Residential Energy Standard, PNNL 1989).

In 1990, I worked briefly for a East Coast consultant company that did a lot of
utility-supported DSM projects. For those, we would typically build a base building
model, and then ran it through a dozen or more EEMs requested by the A/E Team.

In 2004, I had a project to calculate U/SHGC trade-off equations for DOE's EnergyStar
Windows, for which I wrote a batch process for iterative DOE-2 simulations varying the
U-factor until the building energy use was within 0.02MBTU of the EnergyStar Window. The
same procedure could also be used to search for the minimum building energy cost, or
automated optimization.

What I'm trying to say is that parametric analysis has always been around, and not
particularly difficult to do as long as we move away from using GUI interfaces to writing
building input files with macros, and then add scripts to the batch file to set the macros
in the run stream.

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
(o) (925)388-0265
(c) (510)928-2683
"building energy simulations at your fingertips"

Joe Huang's picture
Offline
Joined: 2011-09-30
Reputation: 406

FYI:

Many of the web-based, code compliant programs, such as IC3 (http://ic3.tamu.edu/) are actually using parameters and/or an include file to do their business. So, one might say that much of the work in this area continues, but under another umbrella. Feel free to login and check it out. Information about the development of IC3 can be found on our Lab's web site (http://esl.tamu.edu/terp/reports), including a sample input file that delivers a code-compliant building.

Jeff

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
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
8=/ 8=) :=) 8=) ;=) 8=) 8=() 8=) :=) 8=) 8=! 8=) 8=? 8=) 8=0

Jeff Haberl2's picture
Offline
Joined: 2011-10-02
Reputation: 200

Aaron, Jason,

When it comes to multiple parameter optimisation, I have previously done that for residential building heating and cooling. Idea was to design the optimal characteristics of the heating and cooling devices in a residential zone (a room).

On https://lirias.kuleuven.be/handle/1979/2651 at the bottom, you can download the PhD. Chapter 7 explains the aspects of multiple parameter optimisation. Chapter 8 gives examples.

Main thing is that there might be many ways to reach the same value of the objective function near and in the optimum. One composition might lead to a more feasible or cheaper optimum compared to the other.

Leen

leen peeters's picture
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Joined: 2011-10-02
Reputation: 0

Almost completely unrelated to the rest of the thread, but this has been bugging me every time I see it:
[cid:image001.png at 01D0EED6.8D9BE2C0]
It?s like you found the magic WWR for that building! Obviously you can draw reasonable conclusions from that chart while ignoring the obvious outlier point, but you know, it just looks? wrong.

Nathan Miller, PE, LEED AP BD+C ? Mechanical Engineer/Senior Energy Analyst
RUSHING | D 206-788-4577 | O 206-285-7100
www.rushingco.com

Nathan Miller's picture
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Joined: 2011-09-30
Reputation: 200

I noticed that the EnergyPlus Example File Generator (
http://apps1.eere.energy.gov/buildings/energyplus/cfm/inputs/index.cfm#form_generator),
in the "detailed" mode, includes a section on Building Geometry. This
includes building shape, zone layout, and number of floors.

Can this be automated, so that instead of the designer choosing a layout
blindly, the Generator runs *all* the possible layout configurations and
returns the results? Prescriptive defaults for building systems would be
fixed since we're primarily interested in form. This would be a starting
place for automating the optimization of form, in the concept design phase,
as we do for parametric optimization of components in the design
development phase. Dan J

(6 shapes) x (9 aspect ratios each) x (3 zoning concepts each) x (3
conditioning strategies each) = only 486 simulations. Take the 20 best
performers, and the architect still has broad creative freedom within the
set, but is guaranteed a better performance outcome than starting blindly.
:-)

Dan Johnson | Design and Energy | 510.325.5672
Assoc. AIA, ASHRAE, LEED AP, CEPE, CPHC | 907 Ramona Ave. Albany California
94706

Dan Johnson's picture
Offline
Joined: 2015-09-11
Reputation: 0

Jason,

This is something I'm interested in as well. I think all the preliminary
design factors that you mentioned are great things to look at. On the
later parts of the design process, control parameters are also good things
to look at (CHW plant control optimization, air-side control optimization,
etc.).

One way to convey the information is through simple 2D plots. Below is an
example of 200 DOE2 simulations while varying the window to wall ratio and
another plot of 81 simulations varying the window shading coefficient.

[image: Inline image 1][image: Inline image 2]

Unfortunately, this does not convey the interactive nature of optimization
over multiple variables. Using multidimensional optimization algorithms
can be another useful tool, but they can be tricky. As an example, below
is a case of looking for the optimal minimum condenser water flow in a
variable flow condenser system. From looking at the first plot, the
function seems relatively smooth and it's obvious that there's an optimal
in the neighborhood of 0.6. However, if you zoom in (second plot), you can
see that the data is not very smooth, and there are all kinds of jagged
local minima/maxima. These will tend to throw off most optimization
algorithms, which is why I think it's helpful to consider looking at
automated mass simulations before taking on the problem of optimization.

[image: Inline image 5]

[image: Inline image 4]
Aaron

Aaron Powers2's picture
Offline
Joined: 2011-09-30
Reputation: 0

Aaron and Jason, I like this thread. I noticed that the curve Aaron
presented for WWR vs. Source Energy shows only a 4% difference in Source
Energy over a range of 0-50% WWR---I would say a negligible difference from
optimizing this component alone. An architect would shrug this off, despite
the thought and computing power that went into it. This got me to thinking
that, as an architect, the aspect my colleagues most have trouble with is
the arrangement of building elements, rather than the optimization of each
one.

Jason's questions related to arrangement---how many floors? what shape
building?---would be the most useful for conceptual design, in my opinion.
I would leave alone insulation levels and even WWR at the
programming/conceptual stage, and instead run hundreds of simulations with
different *arrangements* of zones. I would further clarify this point by
adding to Jason's questions list:

1. Does the conference room go on the east or west (re: sun exposure)?
Under the roof, or in the basement?
2. How deep can floor plates be, and still achieve adequate daylighting?
3. What aspect ratio of the atrium vs. the floor plates gives us adequate
natural ventilation?
4. If I put the fume hoods in the classroom, I have to ventilate the whole
classroom, but if I put them only in prep rooms, that is a much smaller air
volume...what difference does this make?
5. If I cluster all my public circulation to one side, and naturally
ventilate this, how much energy do I save vs. distributed circulation that
is conditioned with fans?
6. Add a giant, beautiful glass staircase---do I save enough elevator
energy to offset the conditioning of the staircase?

In typical optimization modeling, the geometry is fixed and we vary the
component parameters. I'd like to see the opposite.

If the auto-generating algorithm could produce a 3D mass diagram of
color-coded zone blobs, perhaps as a Sketchup object, that would
communicate well.

Again, architects tend to have trouble with the spatial arrangement of
zones as something driven by performance. Perhaps the most useful thing at
the programming/concept design stage is help with arrangement. Thank you,
Dan J

Dan Johnson | Design and Energy | 510.325.5672
Assoc. AIA, ASHRAE, LEED AP, CEPE, CPHC | 907 Ramona Ave. Albany California
94706

Dan Johnson's picture
Offline
Joined: 2015-09-11
Reputation: 0

Dan,
You are right that most of the 'optimization' happens once all the
massing/shape/room locations/configurations are finalized. Even now, I
dread when I am asked to help with energy analysis during the early design
phase since I have this at the back of the mind that the architect might
come back with several totally new designs (shape/floors etc.) and I will
have to redo the whole process. This has never happened in my case, and
most decisions are WWR, insulation, glass types, shading and major system
types.
To get back to your question, for the type of analysis that you have
outlined, you might want to look at the expert system based research in
architecture departments where they work on automatic design generation.
Carnegie Mellon comes to mind since I was involved in this type of work
ages back (that was in the 90's.). Microstation/Autocad might have these
kind of tools inbuilt by now (I havn't really kept up with this topic), and
since they also have energy tools connection - what you are suggesting
seems doable.
-Rohini

R B's picture
R B
Offline
Joined: 2011-09-30
Reputation: 4

Hi Jason and Aaron

We've been working on this subject for a year or two, and we are confident that automated building simulations augmented by uncertainty analysis (UA) and sensitivity analysis (SA) have significant potential for the industry. In our research, we perform Monte Carlo simulations using a normative ISO 13790 model to investigate a vast, global design space in relation to energy consumption, thermal comfort, and daylight availability.

The iterative work flow is as follows:

1. Assign probability distributions to a wide range of design parameters believed to impact performance

2. Create sample matrix from distributions

3. Run Monte Carlo simulations (1000?s)

4. Perform UA, SA and multivariate analysis

During the early design we learn about:

- SA helps identify the most influential inputs at the particular stage in the design process. The design team may then focus on these design parameters having the largest impact on the output

- UA identifies best case and worst case scenarios under the given design variability

- Using Monte Carlo filtering the designer may apply filters (criteria/constraints) on one or more outputs to show which favorable regions of the design space are most likely to produce acceptable results

- If the simulationist has explored a sufficiently large design space, hundreds of the simulations will meet the requirements. In that case, Monte Carlo filtering may also be used for inputs to see the consequences of different design limits/choices. This is useful during workshops and meetings where the different actors are present (building owner, architects, engineers, contractors).

In a late, detailed design situation the designer may apply uncertainties to user behavior, time schedule, weather, etc. This will reveal the following:

- Again, SA identifies the inputs having most input on output variability. The designer can then try to gain more knowledge about these inputs to reduce variance of the output

- UA give more reliable results about the expected performance of the building. This may help reduce performance gaps.

We will present some of our work at the international IBPSA conference in India in December (?A stochastic and holistic method to support decision-making in early building design?).

P.S.

Preliminary screening methods, such as Morris Method (or method of elementary effects), may be performed to ?identify factors in the model which, left free to vary over their range of uncertainty, make no significant contribution to the variance of the output? [1]. This will

P.P.S.

Note about optimisation:

In the early stages no optimisation is performed since this may conflict with the interest of other stakeholders (building owners, architects, engineers, contractors). This may also conflict with qualitative measures such as aesthetics, function, constructions, etc.

In late design, optimisation is highly relevant to optimize on building controls, HVAC system, etc.

Best regards,

Torben ?sterg?rd

Industrial Ph.D. student at Aalborg University

MSc. Architectural Engineering at MOE A/S

[1] A. Saltelli, et al. (2008), Global sensitivity analysis: the primer. Wiley & Sons

________________________________
Fra: Aaron Powers [caaronpowers at gmail.com]
Sendt: 10. september 2015 19:46
Til: Jason Glazer
Cc: bldg-sim at onebuilding.org
Emne: Re: [Bldg-sim] 100 simulations

Jason,

This is something I'm interested in as well. I think all the preliminary design factors that you mentioned are great things to look at. On the later parts of the design process, control parameters are also good things to look at (CHW plant control optimization, air-side control optimization, etc.).

One way to convey the information is through simple 2D plots. Below is an example of 200 DOE2 simulations while varying the window to wall ratio and another plot of 81 simulations varying the window shading coefficient.

[Inline image 1][Inline image 2]

Unfortunately, this does not convey the interactive nature of optimization over multiple variables. Using multidimensional optimization algorithms can be another useful tool, but they can be tricky. As an example, below is a case of looking for the optimal minimum condenser water flow in a variable flow condenser system. From looking at the first plot, the function seems relatively smooth and it's obvious that there's an optimal in the neighborhood of 0.6. However, if you zoom in (second plot), you can see that the data is not very smooth, and there are all kinds of jagged local minima/maxima. These will tend to throw off most optimization algorithms, which is why I think it's helpful to consider looking at automated mass simulations before taking on the problem of optimization.

[Inline image 5]

[Inline image 4]
Aaron

=?Windows-1252?Q?Torben_=D8stergaard?='s picture
Joined: 2015-09-12
Reputation: 0

Parametric study of building variations is perhaps where computer simulations are at their
best in playing the what-if game!

It's troubling and unfortunate that there was more such studies back 20-30 years ago than
now, where the focus has been on developing ever more complicated building models and ever
more detailed algorithms, with the apparent goal of creating pristine one-time-use
building models.

I remember when I was first introduced to building energy simulations at UC Berkeley in
1980, one of my class assignments was to use Murray Milne's SOLAR-5 program to study the
change in loads for different building orientations. When I first went to LBNL (LBL in
those days :-)), I created a residential building energy data base for which I created 5
prototypical residential building models full of macros, which allowed me to do a hundred
parametric for each building model in 45 locations, stepping through common variations of
ceiling and wall insulation, window area/orientation/panes of glass, infiltration rates,
etc., or around 10,000-20,000 runs in all. These were then reduced to nonlinear equations
that drove simplified programs such as PEAR (Program for Energy Analysis of Residences,
LBNL 1987) or ARES (Automated Residential Energy Standard, PNNL 1989).

In 1990, I worked briefly for a East Coast consultant company that did a lot of
utility-supported DSM projects. For those, we would typically build a base building
model, and then ran it through a dozen or more EEMs requested by the A/E Team.

In 2004, I had a project to calculate U/SHGC trade-off equations for DOE's EnergyStar
Windows, for which I wrote a batch process for iterative DOE-2 simulations varying the
U-factor until the building energy use was within 0.02MBTU of the EnergyStar Window. The
same procedure could also be used to search for the minimum building energy cost, or
automated optimization.

What I'm trying to say is that parametric analysis has always been around, and not
particularly difficult to do as long as we move away from using GUI interfaces to writing
building input files with macros, and then add scripts to the batch file to set the macros
in the run stream.

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
(o) (925)388-0265
(c) (510)928-2683
"building energy simulations at your fingertips"

Joe Huang's picture
Offline
Joined: 2011-09-30
Reputation: 406

FYI:

Many of the web-based, code compliant programs, such as IC3 (http://ic3.tamu.edu/) are actually using parameters and/or an include file to do their business. So, one might say that much of the work in this area continues, but under another umbrella. Feel free to login and check it out. Information about the development of IC3 can be found on our Lab's web site (http://esl.tamu.edu/terp/reports), including a sample input file that delivers a code-compliant building.

Jeff

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
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
8=/ 8=) :=) 8=) ;=) 8=) 8=() 8=) :=) 8=) 8=! 8=) 8=? 8=) 8=0

Jeff Haberl2's picture
Offline
Joined: 2011-10-02
Reputation: 200

Aaron, Jason,

When it comes to multiple parameter optimisation, I have previously done that for residential building heating and cooling. Idea was to design the optimal characteristics of the heating and cooling devices in a residential zone (a room).

On https://lirias.kuleuven.be/handle/1979/2651 at the bottom, you can download the PhD. Chapter 7 explains the aspects of multiple parameter optimisation. Chapter 8 gives examples.

Main thing is that there might be many ways to reach the same value of the objective function near and in the optimum. One composition might lead to a more feasible or cheaper optimum compared to the other.

Leen

leen peeters's picture
Offline
Joined: 2011-10-02
Reputation: 0

Almost completely unrelated to the rest of the thread, but this has been bugging me every time I see it:
[cid:image001.png at 01D0EED6.8D9BE2C0]
It?s like you found the magic WWR for that building! Obviously you can draw reasonable conclusions from that chart while ignoring the obvious outlier point, but you know, it just looks? wrong.

Nathan Miller, PE, LEED AP BD+C ? Mechanical Engineer/Senior Energy Analyst
RUSHING | D 206-788-4577 | O 206-285-7100
www.rushingco.com

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I noticed that the EnergyPlus Example File Generator (
http://apps1.eere.energy.gov/buildings/energyplus/cfm/inputs/index.cfm#form_generator),
in the "detailed" mode, includes a section on Building Geometry. This
includes building shape, zone layout, and number of floors.

Can this be automated, so that instead of the designer choosing a layout
blindly, the Generator runs *all* the possible layout configurations and
returns the results? Prescriptive defaults for building systems would be
fixed since we're primarily interested in form. This would be a starting
place for automating the optimization of form, in the concept design phase,
as we do for parametric optimization of components in the design
development phase. Dan J

(6 shapes) x (9 aspect ratios each) x (3 zoning concepts each) x (3
conditioning strategies each) = only 486 simulations. Take the 20 best
performers, and the architect still has broad creative freedom within the
set, but is guaranteed a better performance outcome than starting blindly.
:-)

Dan Johnson | Design and Energy | 510.325.5672
Assoc. AIA, ASHRAE, LEED AP, CEPE, CPHC | 907 Ramona Ave. Albany California
94706

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