Handling model uncertainty in model predictive control for energy efficient buildings
Mehdi Maasoumy, Meysam Razmara, Mahdi Shahbakhti, Alberto Sangiovanni-Vincentelli

Citation
Mehdi Maasoumy, Meysam Razmara, Mahdi Shahbakhti, Alberto Sangiovanni-Vincentelli. "Handling model uncertainty in model predictive control for energy efficient buildings". Energy and Buildings, 77:377-392, July 2014; http://www.sciencedirect.com/science/article/pii/S0378778814002771.

Abstract
Model uncertainty is a significant challenge to more widespread use of model predictive controllers (MPC) for optimizing building energy consumption. This paper presents two methodologies to handle model uncertainty for building MPC. First, we propose a modeling framework for online estimation of states and unknown parameters leading to a parameter-adaptive building (PAB) model. Second, we propose a robust model predictive control (RMPC) formulation to make a building controller robust to model uncertainties. The results from these two approaches are compared with those from a nominal MPC and a common building rule based control (RBC). The results are then used to develop a methodology for selecting a controller type (i.e. RMPC, MPC, or RBC) as a function of building model uncertainty. RMPC is found to be the superior controller for the cases with an intermediate level of model uncertainty (30-67%), while the nominal MPC is preferred for the cases with a low level of model uncertainty (0-30%). Further, a common RBC outperforms MPC or RMPC if the model uncertainty goes beyond a certain threshold (e.g. 67%).

Electronic downloads

Citation formats  
  • HTML
    Mehdi Maasoumy, Meysam Razmara, Mahdi Shahbakhti, Alberto
    Sangiovanni-Vincentelli. <a
    href="http://www.terraswarm.org/pubs/310.html"
    >Handling model uncertainty in model predictive control
    for energy efficient buildings</a>, <i>Energy
    and Buildings</i>, 77:377-392, July 2014;
    http://www.sciencedirect.com/science/article/pii/S0378778814002771.
  • Plain text
    Mehdi Maasoumy, Meysam Razmara, Mahdi Shahbakhti, Alberto
    Sangiovanni-Vincentelli. "Handling model uncertainty in
    model predictive control for energy efficient
    buildings". <i>Energy and Buildings</i>,
    77:377-392, July 2014;
    http://www.sciencedirect.com/science/article/pii/S0378778814002771.
  • BibTeX
    @article{MaasoumyRazmaraShahbakhtiSangiovanniVincentelli14_HandlingModelUncertaintyInModelPredictiveControlForEnergy,
        author = {Mehdi Maasoumy and Meysam Razmara and Mahdi
                  Shahbakhti and Alberto Sangiovanni-Vincentelli},
        title = {Handling model uncertainty in model predictive
                  control for energy efficient buildings},
        journal = {Energy and Buildings},
        volume = {77},
        pages = {377-392},
        month = {July},
        year = {2014},
        note = {http://www.sciencedirect.com/science/article/pii/S0378778814002771},
        abstract = {Model uncertainty is a significant challenge to
                  more widespread use of model predictive
                  controllers (MPC) for optimizing building energy
                  consumption. This paper presents two methodologies
                  to handle model uncertainty for building MPC.
                  First, we propose a modeling framework for online
                  estimation of states and unknown parameters
                  leading to a parameter-adaptive building (PAB)
                  model. Second, we propose a robust model
                  predictive control (RMPC) formulation to make a
                  building controller robust to model uncertainties.
                  The results from these two approaches are compared
                  with those from a nominal MPC and a common
                  building rule based control (RBC). The results are
                  then used to develop a methodology for selecting a
                  controller type (i.e. RMPC, MPC, or RBC) as a
                  function of building model uncertainty. RMPC is
                  found to be the superior controller for the cases
                  with an intermediate level of model uncertainty
                  (30-67%), while the nominal MPC is preferred for
                  the cases with a low level of model uncertainty
                  (0-30%). Further, a common RBC outperforms MPC or
                  RMPC if the model uncertainty goes beyond a
                  certain threshold (e.g. 67%).},
        URL = {http://terraswarm.org/pubs/310.html}
    }
    

Posted by Mehdi Maasoumy on 10 May 2014.

Notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright.