Low Cost Building Model Capture for Energy-Efficient Model Based Control
Madhur Behl

Citation
Madhur Behl. "Low Cost Building Model Capture for Energy-Efficient Model Based Control". Tutorial, 21, January, 2015; The eWorkshop is scheduled for 10:00am-11:00am Pacific / 1:00pm-2:00pm ET.

Abstract
One of the biggest challenges in the design of closed-loop Cyber-Physical Systems (CPS) is in accurately capturing the dynamics of the underlying physical system. In the context of buildings, the modeling difficulty arises due to the fact that each building is designed and used in a different way and therefore it has to be uniquely modeled (Heterogeneity). Furthermore, each building system consists of a large number of interconnected subsystems that interact in a complex manner and are subjected to time varying environmental conditions (Complexity). The focus of this work is on: 1. ModelIQ: Inverse Model Accuracy and Control Performance Toolbox for Buildings: Analyzes the propagation of uncertainty from sensor data to model accuracy and further to model based control performance and makes recommendations on which sensors to install, what is their associated cost-benefit and where to place the sensors. 2. Experiment Design for Economical and Autonomous Building Model Training: Data with high information content with respect to model parameters to be estimated should yield more accurate estimates and result in a better model. Moreover, we want to avoid running experiments/functional tests in a building longer than necessary. We successfully automate the model training procedure and find the optimal input signal trajectory that maximizes the information about the model parameters subject to operational constraints that helps us identify the building model in an optimal manner.

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Citation formats

• HTML

  Madhur Behl. <a
  href="http://www.terraswarm.org/pubs/474.html"
  ><i>Low Cost Building Model Capture for
  Energy-Efficient Model Based Control</i></a>,
  Tutorial,  21, January, 2015; The eWorkshop is scheduled for
  10:00am-11:00am Pacific / 1:00pm-2:00pm ET.

• Plain text

  Madhur Behl. "Low Cost Building Model Capture for
  Energy-Efficient Model Based Control". Tutorial,  21,
  January, 2015; The eWorkshop is scheduled for
  10:00am-11:00am Pacific / 1:00pm-2:00pm ET.

• BibTeX

  @tutorial{Behl15_LowCostBuildingModelCaptureForEnergyEfficientModelBased,
      author = {Madhur Behl},
      title = {Low Cost Building Model Capture for
                Energy-Efficient Model Based Control},
      day = {21},
      month = {January},
      year = {2015},
      note = {The eWorkshop is scheduled for 10:00am-11:00am
                Pacific / 1:00pm-2:00pm ET.},
      abstract = {One of the biggest challenges in the design of
                closed-loop Cyber-Physical Systems (CPS) is in
                accurately capturing the dynamics of the
                underlying physical system. In the context of
                buildings, the modeling difficulty arises due to
                the fact that each building is designed and used
                in a different way and therefore it has to be
                uniquely modeled (Heterogeneity). Furthermore,
                each building system consists of a large number of
                interconnected subsystems that interact in a
                complex manner and are subjected to time varying
                environmental conditions (Complexity). The focus
                of this work is on: 1.	ModelIQ: Inverse Model
                Accuracy and Control Performance Toolbox for
                Buildings: Analyzes the propagation of uncertainty
                from sensor data to model accuracy and further to
                model based control performance and makes
                recommendations on which sensors to install, what
                is their associated cost-benefit and where to
                place the sensors. 2.	Experiment Design for
                Economical and Autonomous Building Model Training:
                Data with high information content with respect to
                model parameters to be estimated should yield more
                accurate estimates and result in a better model.
                Moreover, we want to avoid running
                experiments/functional tests in a building longer
                than necessary. We successfully automate the model
                training procedure and find the optimal input
                signal trajectory that maximizes the information
                about the model parameters subject to operational
                constraints that helps us identify the building
                model in an optimal manner.},
      URL = {http://terraswarm.org/pubs/474.html}
  }
  

Posted by Barb Hoversten on 13 Jan 2015.