Model-IQ: Uncertainty Propagation from Sensing to Modeling and Control in Buildings
Madhur Behl, Truong X. Nghiem, Rahul Mangharam

Citation
Madhur Behl, Truong X. Nghiem, Rahul Mangharam. "Model-IQ: Uncertainty Propagation from Sensing to Modeling and Control in Buildings". 5th International Conference on Cyber-Physical Systems., ACM/IEEE, 14, April, 2014.

Abstract
A fundamental problem in the design of closed-loop Cyber-Physical Systems (CPS) is in accurately capturing the dynamics of the underlying physical system. To provide optimal control for such closed-loop systems, model-based controls require accurate physical plant models. It is hard to analytically establish (a) how data quality from sensors affects model accuracy, and consequently, (b) the effect of model accuracy on the operational cost of model-based controllers. We present the Model-IQ toolbox which, given a plant model and real input data, automatically evaluates the effect of this uncertainty propagation from sensor data to model accuracy to controller performance. We apply the Model-IQ uncertainty analysis for model-based controls in buildings to demonstrate the cost-benefit of adding temporary sensors to capture a building model. We show how sensor placement and density bias training data. For the real building considered, a bias of 1% degrades model accuracy by 20%. Model-IQ's automated process lowers the cost of sensor deployment, model training and evaluation of advanced controls for small and medium sized buildings. Such end-to-end analysis of uncertainty propagation has the potential to lower the cost for CPS with closed-loop model based control. We demonstrate this with real building data in the Department of Energy's HUB.

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Citation formats  
  • HTML
    Madhur Behl, Truong X. Nghiem, Rahul Mangharam. <a
    href="http://www.terraswarm.org/pubs/245.html"
    >Model-IQ: Uncertainty Propagation from Sensing to
    Modeling and Control in Buildings</a>, 5th
    International Conference on Cyber-Physical Systems., 
    ACM/IEEE, 14, April, 2014.
  • Plain text
    Madhur Behl, Truong X. Nghiem, Rahul Mangharam.
    "Model-IQ: Uncertainty Propagation from Sensing to
    Modeling and Control in Buildings". 5th International
    Conference on Cyber-Physical Systems.,  ACM/IEEE, 14, April,
    2014.
  • BibTeX
    @inproceedings{BehlNghiemMangharam14_ModelIQUncertaintyPropagationFromSensingToModeling,
        author = {Madhur Behl and Truong X. Nghiem and Rahul
                  Mangharam},
        title = {Model-IQ: Uncertainty Propagation from Sensing to
                  Modeling and Control in Buildings},
        booktitle = {5th International Conference on Cyber-Physical
                  Systems.},
        organization = { ACM/IEEE},
        day = {14},
        month = {April},
        year = {2014},
        abstract = {A fundamental problem in the design of closed-loop
                  Cyber-Physical Systems (CPS) is in accurately
                  capturing the dynamics of the underlying physical
                  system. To provide optimal control for such
                  closed-loop systems, model-based controls require
                  accurate physical plant models. It is hard to
                  analytically establish (a) how data quality from
                  sensors affects model accuracy, and consequently,
                  (b) the effect of model accuracy on the
                  operational cost of model-based controllers. We
                  present the Model-IQ toolbox which, given a plant
                  model and real input data, automatically evaluates
                  the effect of this uncertainty propagation from
                  sensor data to model accuracy to controller
                  performance. We apply the Model-IQ uncertainty
                  analysis for model-based controls in buildings to
                  demonstrate the cost-benefit of adding temporary
                  sensors to capture a building model. We show how
                  sensor placement and density bias training data.
                  For the real building considered, a bias of 1%
                  degrades model accuracy by 20%. Model-IQ's
                  automated process lowers the cost of sensor
                  deployment, model training and evaluation of
                  advanced controls for small and medium sized
                  buildings. Such end-to-end analysis of uncertainty
                  propagation has the potential to lower the cost
                  for CPS with closed-loop model based control. We
                  demonstrate this with real building data in the
                  Department of Energy's HUB.},
        URL = {http://terraswarm.org/pubs/245.html}
    }
    

Posted by Madhur Behl on 23 Jan 2014.

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