**Page description appears here**

“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 of South-Eastern Norway
Reference: 2016, Vol 37, No 3, pp. 171-180.

     Valid XHTML 1.0 Strict

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

DOI forward links to this article:
  [1] Degurunnehalage Perera and Nils-Olav Skeie (2017), doi:10.3390/buildings7020027
  [2] Pin Lyu, Sheng Bao, Jizhou Lai, Shichao Liu and Zang Chen (2018), doi:10.1177/0954410018767754

[1] Bacher, P. and Madsen, H. (2011). Bacher, P, and Madsen, H. Identifying suitable models for the heat dynamics of buildings. Energy and Buildings. 43(7):1511--1522. doi:10.1016/j.enbuild.2011.02.005
[2] Dounis, A. and Caraiscos, C. (2009). Dounis, A, and Caraiscos, C. Advanced control systems engineering for energy and comfort management in a building environment-a review. Renewable and Sustainable Energy Reviews. 13(6-7):1246--1261. doi:10.1016/j.rser.2008.09.015
[3] Fux, S.F., Ashouri, A., Benz, M.J., and Guzzella, L. (2014). Fux, S, F., Ashouri, A., Benz, M.J., and Guzzella, L. Ekf based self-adaptive thermal model for a passive house. Energy and Buildings. 68, Part C:811--817. doi:10.1016/j.enbuild.2012.06.016
[4] Kristensen, N., Madsen, H., and Jørgensen, S. (2004). Kristensen, N, , Madsen, H., and Jørgensen, S. Parameter estimation in stochastic grey-box models. Automatica. 40(2):225--237. doi:10.1016/j.automatica.2003.10.001
[5] Maasoumy, M., Moridian, B., Razmara, M., Shahbakhti, M., and Sangiovanni-Vincentelli, A. (2013). Maasoumy, M, , Moridian, B., Razmara, M., Shahbakhti, M., and Sangiovanni-Vincentelli, A. Online simultaneous state estimation and parameter adaptation for building predictive control. In ASME 2013 Dynamic Systems and Control Conference2013. 2013. doi:10.1115/DSCC2013-4064
[6] Madsen, H. and Holst, J. (1995). Madsen, H, and Holst, J. Estimation of continuous-time models for the heat dynamics of a building. Energy and buildings. 22(1):67--79. doi:10.1016/0378-7788(94)00904-X
[7] Martincevic, A., Starcic, A., and Vasak, M. (2014). Martincevic, A, , Starcic, A., and Vasak, M. Parameter estimation for low-order models of complex buildings. In Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), 2014 IEEE PES. 2014. doi:10.1109/ISGTEurope.2014.7028767
[8] Perera, D. W.U., Pfeiffer, C., and Skeie, N.-O. (2014). Perera, D, W.U., Pfeiffer, C., and Skeie, N.-O. Modelling the heat dynamics of a residential building unit: Application to norwegian buildings. Modeling, Identification and Control, 2014. 35(1):43--57. doi:10.4173/mic.2014.1.4
[9] Perera, M. A.S., Lie, B., and Pfeiffer, C.F. (2015). Perera, M, A.S., Lie, B., and Pfeiffer, C.F. Structural observability analysis of large scale systems using modelica and python. Modeling Identification and Control. 36(1):53--65. doi:10.4173/mic.2015.1.4
[10] Perera, W., Pfeiffer, C.F., and Skeie, N.-O. (2014). Perera, W, , Pfeiffer, C.F., and Skeie, N.-O. Modeling and simulation of multi-zone buildings for better control. In 55th Conference on Simulation and Modelling, Aalborg, Denmark: Linköping University Electronic Press. 2014. .
[11] Perez-Lombard, L., Ortiz, J., and Pout, C. (2008). Perez-Lombard, L, , Ortiz, J., and Pout, C. A review on buildings energy consumption information. Energy and Buildings. 40:394--398. .
[12] Radecki, P. and Hencey, B. (2012). Radecki, P, and Hencey, B. Online building thermal parameter estimation via unscented kalman filtering. In American Control Conference (ACC). 2012. doi:10.1109/ACC.2012.6315699
[13] Simon, D. (2006). Simon, D, Optimal state estimation : Kalman, H Infinity and nonlinear approaches. Wiley-Interscience. .
[14] Walker, D.M. (2005). Walker, D, M. System identification using constrained kalman filters. In International Symposium on Nonlinear Theory and its Applications. 2005. .

  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}


Oct 2018: MIC reaches 3000 DOI Forward Links. The last 1000 took 2 years and 5 months.

May 2016: MIC reaches 2000 DOI Forward Links. The first 1000 took 34 years, the next 1000 took 2.5 years.

July 2015: MIC's new impact factor is now 0.778. The number of papers published in 2014 was 21 compared to 15 in 2013, which partially explains the small decrease in impact factor.

Aug 2014: For the 3rd year in a row MIC's impact factor increases. It is now 0.826.

Dec 2013: New database-driven web-design enabling extended statistics. Article number 500 is published and MIC reaches 1000 DOI Forward Links.

Jan 2012: Follow MIC on your smartphone by using the RSS feed.


July 2011: MIC passes 1000 ISI Web of Science citations.

Mar 2010: MIC is now indexed by DOAJ and has received the Sparc Seal seal for open access journals.

Dec 2009: A MIC group is created at LinkedIn and Twitter.

Oct 2009: MIC is now fully updated in ISI Web of Knowledge.