Online Simultaneous State Estimation and Parameter Adaptation for Building Predictive Control
Mehdi Maasoumy, Barzin Moridian, Meysam Razmara, Mahdi Shahbakhti, Alberto Sangiovanni-Vincentelli

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), ASME, 21, November, 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, we propose a modeling framework for on-line estimation of states and unknown parameters of buildings, leading to the Parameter-Adaptive Building (PAB) model. 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.

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  • HTML
    Mehdi Maasoumy, Barzin Moridian, Meysam Razmara, Mahdi
    Shahbakhti, Alberto Sangiovanni-Vincentelli. <a
    href="http://www.terraswarm.org/pubs/77.html"
    >Online Simultaneous State Estimation and Parameter
    Adaptation for Building Predictive Control</a>,
    Dynamic Systems and Control Conference (DSCC 2013), ASME,
    21, November, 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), ASME, 21, November, 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)},
        organization = {ASME},
        day = {21},
        month = {November},
        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, we propose a
                  modeling framework for on-line estimation of
                  states and unknown parameters of buildings,
                  leading to the Parameter-Adaptive Building (PAB)
                  model. 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/77.html}
    }
    

Posted by Mehdi Maasoumy on 2 Jul 2013.

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