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“Structural observability analysis and EKF based parameter estimation of building heating models”

Authors: D.W.U. Perera, M. Anushka S. Perera, Carlos F. Pfeiffer and Nils-Olav Skeie,
Affiliation: University College of Southeast Norway
Reference: 2016, Vol 37, No 3, pp. 171-180.

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Keywords: Extended Kalman Filter, Mathematical models, Parameter estimation, Single-zone building, Structural observability

Abstract: Research for enhanced energy-efficient buildings has been given much recognition in the recent years owing to their high energy consumptions. Increasing energy needs can be precisely controlled by practicing advanced controllers for building Heating, Ventilation, and Air-Conditioning (HVAC) systems. Advanced controllers require a mathematical building heating model to operate, and these models need to be accurate and computationally efficient. One main concern associated with such models is the accurate estimation of the unknown model parameters. This paper presents the feasibility of implementing a simplified building heating model and the computation of physical parameters using an off-line approach. Structural observability analysis is conducted using graph-theoretic techniques to analyze the observability of the developed system model. Then Extended Kalman Filter (EKF) algorithm is utilized for parameter estimates using the real measurements of a single-zone building. The simulation-based results confirm that even with a simple model, the EKF follows the state variables accurately. The predicted parameters vary depending on the inputs and disturbances.

PDF PDF (1231 Kb)        DOI: 10.4173/mic.2016.3.3

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  title={{Structural observability analysis and EKF based parameter estimation of building heating models}},
  author={Perera, D.W.U. and Perera, M. Anushka S. and Pfeiffer, Carlos F. and Skeie, Nils-Olav},
  journal={Modeling, Identification and Control},
  publisher={Norwegian Society of Automatic Control}


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