Citation
Anil Aswani, Neal Master, Jay Taneja, Virginia Smith, Andrew Krioukov, David Culler, Claire Tomlin. "Identifying Models of HVAC Systems Using Semiparametric Regression". 2012 American Control Conference, 28, June, 2012.

Abstract
Heating, ventilation, and air-conditioning (HVAC) systems use a large amount of energy, and so they are an interesting area for efficiency improvements. The focus here is on the use of semiparametric regression to identify models, which are amenable to analysis and control system design, of HVAC systems. This paper briefly describes two testbeds that we have built on the Berkeley campus for modeling and efficient control of HVAC systems, and we use these testbeds as case studies for system identification. The main contribution of this work is that the use of semiparametric regression allows for the estimation of the heating load from occupancy, equipment, and solar heating using only temperature measurements. These estimates are important for building accurate models as well as designing efficient control schemes, and in our other work we have been able to achieve a reduction in energy consumption on a single room testbed using heating load estimation in conjunction with the learning-based model predictive control (LBMPC) technique. Furthermore, this framework is not restrictive to modeling nonlinear HVAC behavior, because we have been able to use this methodology to create hybrid system models that incorporate such nonlinearities.

Electronic downloads

• http://www.eecs.berkeley.edu/~aaswani/acc_brite_2012.pdf

• HTML

  Anil Aswani, Neal Master, Jay Taneja, Virginia Smith, Andrew
  Krioukov, David Culler, Claire Tomlin. <a
  href="http://chess.eecs.berkeley.edu/pubs/904.html"
  >Identifying Models of HVAC Systems Using Semiparametric
  Regression</a>, 2012 American Control Conference, 28,
  June, 2012.

• Plain text

  Anil Aswani, Neal Master, Jay Taneja, Virginia Smith, Andrew
  Krioukov, David Culler, Claire Tomlin. "Identifying
  Models of HVAC Systems Using Semiparametric
  Regression". 2012 American Control Conference, 28,
  June, 2012.

• BibTeX

  @inproceedings{AswaniMasterTanejaSmithKrioukovCullerTomlin12_IdentifyingModelsOfHVACSystemsUsingSemiparametricRegression,
      author = {Anil Aswani and Neal Master and Jay Taneja and
                Virginia Smith and Andrew Krioukov and David
                Culler and Claire Tomlin},
      title = {Identifying Models of HVAC Systems Using
                Semiparametric Regression},
      booktitle = {2012 American Control Conference},
      day = {28},
      month = {June},
      year = {2012},
      abstract = {Heating, ventilation, and air-conditioning (HVAC)
                systems use a large amount of energy, and so they
                are an interesting area for efficiency
                improvements. The focus here is on the use of
                semiparametric regression to identify models,
                which are amenable to analysis and control system
                design, of HVAC systems. This paper briefly
                describes two testbeds that we have built on the
                Berkeley campus for modeling and efficient control
                of HVAC systems, and we use these testbeds as case
                studies for system identification. The main
                contribution of this work is that the use of
                semiparametric regression allows for the
                estimation of the heating load from occupancy,
                equipment, and solar heating using only
                temperature measurements. These estimates are
                important for building accurate models as well as
                designing efficient control schemes, and in our
                other work we have been able to achieve a
                reduction in energy consumption on a single room
                testbed using heating load estimation in
                conjunction with the learning-based model
                predictive control (LBMPC) technique. Furthermore,
                this framework is not restrictive to modeling
                nonlinear HVAC behavior, because we have been able
                to use this methodology to create hybrid system
                models that incorporate such nonlinearities.},
      URL = {http://chess.eecs.berkeley.edu/pubs/904.html}
  }
  

Posted by Christopher Brooks on 10 Apr 2012.
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