Citation
Madhur Behl, Truong X. Nghiem, Rahul Mangharam. "IMpACT: Inverse Model Accuracy and Control Performance Toolbox for Buildings". International Conference on Automation Science and Engineering, IEEE, August, 2014.

Abstract
Uncertainty affects all aspects of building performance: from the identification of models, through the implementation of model-based control, to the operation of the deployed systems. Learning models of buildings from sensor data has a fundamental property that the model can only be as accurate and reliable as the data on which it was trained. For small and medium size buildings, a low-cost method for model capture is necessary to take advantage of optimal model-based supervisory control schemes. We present IMpACT, a methodology and a toolbox for analysis of uncertainty propagation for building inverse modeling and controls. Given a plant model and real input data, IMpACT automatically evaluates the effect of the uncertainty propagation from sensor data to model accuracy and control performance. We also present a statistical method to quantify the bias in the sensor measurement and to determine near optimal sensor placement and density for accurate signal measurements. In our previous work, we considered the end-to-end propagation of uncertainty in the form of fixed bias in the sensor data. In this paper, we extend the method to work with random errors in the sensor data, which is more realistic. Using a real building test-bed, we show how performing an uncertainty analysis can reveal trends about inverse model accuracy and control performance, which can be used to make informed decisions about sensor requirements and data accuracy.

Electronic downloads

• http://dx.doi.org/10.1109/CoASE.2014.6899464

• HTML

  Madhur Behl, Truong X. Nghiem, Rahul Mangharam. <a
  href="http://www.terraswarm.org/pubs/313.html"
  >IMpACT: Inverse Model Accuracy and Control Performance
  Toolbox for Buildings</a>, International Conference on
  Automation Science and Engineering, IEEE, August, 2014.

• Plain text

  Madhur Behl, Truong X. Nghiem, Rahul Mangharam.
  "IMpACT: Inverse Model Accuracy and Control Performance
  Toolbox for Buildings". International Conference on
  Automation Science and Engineering, IEEE, August, 2014.

• BibTeX

  @inproceedings{BehlNghiemMangharam14_IMpACTInverseModelAccuracyControlPerformanceToolbox,
      author = {Madhur Behl and Truong X. Nghiem and Rahul
                Mangharam},
      title = {IMpACT: Inverse Model Accuracy and Control
                Performance Toolbox for Buildings},
      booktitle = {International Conference on Automation Science and
                Engineering},
      organization = {IEEE},
      month = {August},
      year = {2014},
      abstract = {Uncertainty affects all aspects of building
                performance: from the identification of models,
                through the implementation of model-based control,
                to the operation of the deployed systems. Learning
                models of buildings from sensor data has a
                fundamental property that the model can only be as
                accurate and reliable as the data on which it was
                trained. For small and medium size buildings, a
                low-cost method for model capture is necessary to
                take advantage of optimal model-based supervisory
                control schemes. We present IMpACT, a methodology
                and a toolbox for analysis of uncertainty
                propagation for building inverse modeling and
                controls. Given a plant model and real input data,
                IMpACT automatically evaluates the effect of the
                uncertainty propagation from sensor data to model
                accuracy and control performance. We also present
                a statistical method to quantify the bias in the
                sensor measurement and to determine near optimal
                sensor placement and density for accurate signal
                measurements. In our previous work, we considered
                the end-to-end propagation of uncertainty in the
                form of fixed bias in the sensor data. In this
                paper, we extend the method to work with random
                errors in the sensor data, which is more
                realistic. Using a real building test-bed, we show
                how performing an uncertainty analysis can reveal
                trends about inverse model accuracy and control
                performance, which can be used to make informed
                decisions about sensor requirements and data
                accuracy.},
      URL = {http://terraswarm.org/pubs/313.html}
  }
  

Posted by Madhur Behl on 16 May 2014.