Citation
Mehdi Maasoumy, Barzin Moridian, Meysam Razmara, Mahdi Shahbakhti, Alberto Sangiovanni-Vincentelli. "Online Simultaneous State Estimation and Parameter Adaptation for Building Predictive Control". Dynamic Systems and Control Conference (DSCC) 2013, Stanford, 21, October, 2013.

Abstract
Model-based control of building energy offers an attractive way to minimize energy consumption in buildings. Model-based controllers require mathematical models that can accurately predict the behavior of the system. For buildings, specifically, these models are difficult to obtain due to highly time varying, and nonlinear nature of building dynamics. Also, model-based controllers often need information of all states, while not all the states of a building model are measurable. In addition, it is challenging to accurately estimate building model parameters (e.g. convective heat transfer coefficient of varying outside air). In this paper, a modeling framework for "on-line estimation" of states and unknown parameters of buildings, leading to the Parameter-Adaptive Building (PAB) model, is proposed. Extended Kalman filter (EKF) and unscented Kalman filter (UKF) techniques are used to design the PAB model which simultaneously tunes the parameters of the model and provides an estimate for all states of the model. The proposed PAB model is tested against experimental data collected from Lakeshore Center building at Michigan Tech University. Our results indicate that the new framework can accurately predict states and parameters of the building thermal model.

Electronic downloads

• http://www.eecs.berkeley.edu/~maasoumy/Research_files/BuildingEstimation.pdf
• http://proceedings.asmedigitalcollection.asme.org/proceeding.aspx?articleid=1841257

• HTML

  Mehdi Maasoumy, Barzin Moridian, Meysam Razmara, Mahdi
  Shahbakhti, Alberto Sangiovanni-Vincentelli. <a
  href="http://www.terraswarm.org/pubs/90.html"
  >Online Simultaneous State Estimation and Parameter
  Adaptation for Building Predictive Control</a>,
  Dynamic Systems and Control Conference (DSCC) 2013,
  Stanford, 21, October, 2013.

• Plain text

  Mehdi Maasoumy, Barzin Moridian, Meysam Razmara, Mahdi
  Shahbakhti, Alberto Sangiovanni-Vincentelli. "Online
  Simultaneous State Estimation and Parameter Adaptation for
  Building Predictive Control". Dynamic Systems and
  Control Conference (DSCC) 2013, Stanford, 21, October, 2013.

• BibTeX

  @inproceedings{MaasoumyMoridianRazmaraShahbakhtiSangiovanniVincentelli13_OnlineSimultaneousStateEstimationParameterAdaptation,
      author = {Mehdi Maasoumy and Barzin Moridian and Meysam
                Razmara and Mahdi Shahbakhti and Alberto
                Sangiovanni-Vincentelli},
      title = {Online Simultaneous State Estimation and Parameter
                Adaptation for Building Predictive Control},
      booktitle = {Dynamic Systems and Control Conference (DSCC)
                2013, Stanford},
      day = {21},
      month = {October},
      year = {2013},
      abstract = {Model-based control of building energy offers an
                attractive way to minimize energy consumption in
                buildings. Model-based controllers require
                mathematical models that can accurately predict
                the behavior of the system. For buildings,
                specifically, these models are difficult to obtain
                due to highly time varying, and nonlinear nature
                of building dynamics. Also, model-based
                controllers often need information of all states,
                while not all the states of a building model are
                measurable. In addition, it is challenging to
                accurately estimate building model parameters
                (e.g. convective heat transfer coefficient of
                varying outside air). In this paper, a modeling
                framework for "on-line estimation" of states and
                unknown parameters of buildings, leading to the
                Parameter-Adaptive Building (PAB) model, is
                proposed. Extended Kalman filter (EKF) and
                unscented Kalman filter (UKF) techniques are used
                to design the PAB model which simultaneously tunes
                the parameters of the model and provides an
                estimate for all states of the model. The proposed
                PAB model is tested against experimental data
                collected from Lakeshore Center building at
                Michigan Tech University. Our results indicate
                that the new framework can accurately predict
                states and parameters of the building thermal
                model. },
      URL = {http://terraswarm.org/pubs/90.html}
  }
  

Posted by Mila MacBain on 13 Aug 2013.