[Bldg-sim] 100 simulations

Fri Sep 11 07:47:16 PDT 2015

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 Østergård

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

On Wed, Sep 9, 2015 at 4:33 PM, Jason Glazer <jglazer at gard.com<mailto:jglazer at gard.com>> wrote:
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<http://onebuilding.org> building performance mailing lists

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