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.

Electronic downloads

• https://www.src.org/library/publication/p069315/. (Link to paper on SRC website.)
• http://dx.doi.org/10.1109/ICCPS.2014.6843707

• 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.