[Bldg-sim] Using Building Simulation and Optimization to Calculate Lookup Tables for Control

[Brian Coffey]

I would like to belatedly announce the posting of my PhD dissertation
entitled Using Building Simulation and Optimization to Calculate Lookup
Tables for Control.

http://escholarship.org/uc/item/1202p562

I would also like to note that the source code download location noted in
the reference list points to a PWGSC ftp site that has since become
password protected, so I have also posted it to the following site:

https://s3.amazonaws.com/SimCon/simcon_v0-2.zip

My apologies for the source code and related tools being less clean than
hoped, and completely lacking in documentation (aside from the
dissertation). I had hoped to rewrite the code and integrate everything as
one coherent tool but time has gotten away from me. Please feel free to
use, modify, rewrite or repackage the source code and tools in any way that
might benefit your own research or practice.

The dissertation abstract is included below. It was completed as part of
the PhD in Architecture (Building Science) at UC Berkeley. The research was
made possible by fellowships from the National Science and Engineering
Research Council of Canada and the American Society of Heating
Refrigeration and Air conditioning Engineers, and through research projects
at the Lawrence Berkeley National Laboratory. Thank you to my dissertation
committee of Gail Brager, Ed Arens, Francesco Borrelli and Philip Haves.

Abstract:
There is a growing demand for more energy efficient buildings. Integrated
systems with more intelligent controls are an important part of meeting
this demand. Model predictive control (MPC) is an established control
technique in other fields and holds promise for improved supervisory
control in buildings. It has been receiving increasing attention in
buildings research but has yet to find its way into common practice. This
is due, at least in part, to a mismatch between the tools and techniques
used in most MPC development and the tools, skills and processes commonly
found in building design and operation. This dissertation investigates an
approach to optimization-based control that uses common building simulation
tools and could fit more readily into building design and operation
practices. Instead of solving optimization problems in real-time to
determine control set-points given current states and predicted
disturbances, the optimal set-points are pre-computed offline over a grid
of possible conditions and the resulting lookup table is used with linear
interpolation for control. The feasibility and range of applicability of
this approach are evaluated, including analyses of the performance impacts
of grid spacing and techniques for problem dimensionality reduction. Three
abstract case studies and two detailed case studies are presented. The
approach is found to be feasible for supervisory control problems that can
be effectively simplified to functions of roughly 5-6 conditions variables,
and the case studies show good performance relative to online MPC. The
benefits for ease of implementation are significant, but the most useful
aspect is likely the feedback it can provide to the design process.


Brian Coffey
Recent PhD grad, Architecture, UC Berkeley
brian.edward.coffey at gmail.com
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