I would like to belatedly announce the posting of my PhD dissertation
entitled Using Building Simulation and Optimization to Calculate Lookup
Tables for Control.<br><br><a href="http://escholarship.org/uc/item/1202p562" target="_blank">http://escholarship.org/uc/item/1202p562</a><br>
<br>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: <br><br>
<a href="https://s3.amazonaws.com/SimCon/simcon_v0-2.zip" target="_blank">https://s3.amazonaws.com/SimCon/simcon_v0-2.zip</a> <br><br>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.<br>
<br>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. <br>
<br>Abstract:<br>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.<div class="yj6qo ajU"><div id=":1jk" class="ajR" tabindex="0"><img class="ajT" src="https://mail.google.com/mail/u/0/images/cleardot.gif"></div></div><span class="HOEnZb adL"><font color="#888888"><br>
<br></font></span><span class="HOEnZb adL"><font color="#888888">Brian Coffey<br>Recent PhD grad, Architecture, UC Berkeley<br><a href="mailto:brian.edward.coffey@gmail.com" target="_blank">brian.edward.coffey@gmail.com</a></font></span>