Identification of Multistorey Building’s Thermal Performance Based on Exponential Filtering

  • Vildan V. Abdullin
  • Dmitry A. Shnayder
  • Lev S. Kazarinov
Conference paper


This work examines the identification of thermal performance of a multistorey building, based on experimental data available for direct measurement. The authors suggest a new model structure with a reduced number of parameters. An identification method based on building an inverse dynamics model that uses exponential filtering is considered. The method makes it possible to estimate signals that cannot be measured directly: the signal of the general perturbation of the indoor air temperature and the signal of specific heat loss through the building envelope. Two examples are given of identifying the thermal performance of a building model: the one based on simulated test data and another one based on real measured data. The identification method proposed in the article puts in a strong performance in both the simulated data model as well as real-data model and may be used in engineering calculations for designing automatic control systems and in predictive control algorithms for heating buildings.


Building thermal conditions model Case study Dynamics model Exponential filtering Heating of buildings Identification Inverse dynamics operator 


  1. 1.
    I.A. Bashmakov, An analysis of the main tendencies in the development of the heating systems in Russia and abroad, (Russian: Анализ основных тенденций развития систем теплоснабжения в России и за рубежом) Novosti teplosnabzhenia 2 (2008) [Online]. Available:
  2. 2.
    J.M. Salmerón, S. Álvarez, J.L. Molina, A. Ruiz, F.J. Sánchez, Tightening the energy consumptions of buildings depending on their typology and on climate severity indexes. Energ. Build. 58, 372–377 (2013)CrossRefGoogle Scholar
  3. 3.
    T. Salsbury, P. Mhaskar, S.J. Qin, Predictive control methods to improve energy efficiency and reduce demand in buildings. Comput. Chem. Eng. 51, 77–85 (2013)CrossRefGoogle Scholar
  4. 4.
    D. Zhou, S.H. Park, Simulation-assisted management and control over building energy efficiency—a case study. Energ. Procedia 14, 592–600 (2012)CrossRefGoogle Scholar
  5. 5.
    P.-D. Moroşan, A distributed MPC strategy based on Benders’ decomposition applied to multi-source multi-zone temperature regulation. J. Process Control 21, 729–737 (2011)CrossRefGoogle Scholar
  6. 6.
    I. Jaffal, C. Inard, C. Ghiaus, Fast method to predict building heating demand based on the design of experiments. Energ. Build. 41, 669–677 (2009)CrossRefGoogle Scholar
  7. 7.
    D.A. Shnayder, V.V. Abdullin, A.A. Basalayev, in Approach to Operations Analysis of Buildings Heat Supply, (Russian: Подход к оперативному анализу эффективности теплоснабжения зданий), Bulletin of the South Ural State University, Series Computer Technologies, Automatic Control, Radio Electronics, vol. 13, 2 (219) 2011, pp. 70–73Google Scholar
  8. 8.
    A.P. Melo, D. Cóstola, R. Lamberts, J.L.M. Hensen, Assessing the accuracy of a simplified building energy simulation model using BESTEST: the case study of Brazilian regulation. Energ. Build. 45, 219–228 (2012)CrossRefGoogle Scholar
  9. 9.
    E. Žáčeková, S. Prívara, Z. Váňa, in AUCC 2011: Model predictive control relevant identification using partial least squares for building modeling, Proceedings of the 2011 Australian Control Conference, pp. 422–427 (Article number 6114301)Google Scholar
  10. 10.
    S. Ginestet, T. Bouache, K. Limam, G. Lindner, Thermal identification of building multilayer walls using reflective Newton algorithm applied to quadrupole modelling. Energ. Build. 60, 139–145 (2013)CrossRefGoogle Scholar
  11. 11.
    S. Prívara, J. Cigler, Z. Váňa, F. Oldewurtel, C. Sagerschnigc, E. Žáčeková, Building modeling as a crucial part for building predictive control. Energ. Build. 56, 8–22 (2013)CrossRefGoogle Scholar
  12. 12.
    Y.A. Tabunschikov, M.M. Brodach, Mathematical modeling and optimization of building thermal efficiency, (Russian: Математическое моделирование и оптимизация тепловой эффективности зданий.)—(Moscow: AVOK-PRESS, 2002)Google Scholar
  13. 13.
    V.V. Abdullin, D.A. Shnayder, L.S. Kazarinov, in WCE 2013: Method of Building Thermal Performance Identification Based on Exponential Filtration. Proceedings of the World Congress on Engineering 2013, London. Lecture Notes in Engineering and Computer Science, 3–5 July, 2013, pp. 2226–2230Google Scholar
  14. 14.
    D.A. Shnayder, L.S. Kazarinov, A method of proactive management of complicated engineering facilities using energy efficiency criteria (Russian: Метод упреждающего управления сложными технологическими комплексами по критериям энергетической эффективности). Manage. Large-Scale Syst. 32, 221–240 (2011)Google Scholar
  15. 15.
    L.S. Kazarinov, S.I. Gorelik, Predicting random oscillatory processes by exponential smoothing (Russian: Прогнозирование случайных колебательных процессов на основе метода экспоненциального сглаживания). Avtomatika i telemekhanika 10, 27–34 (1994)MathSciNetGoogle Scholar
  16. 16.
    D.A. Shnayder, V.V. Abdullin, in TSP 2013: A WSN-based system for heat allocating in multiflat buildings. Proceedings of 36th International Conference on Telecommunications and Signal Processing, Rome, Italy, 2–4 July 2013, pp. 181–185 (Article number 6613915)Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Vildan V. Abdullin
    • 1
  • Dmitry A. Shnayder
    • 1
  • Lev S. Kazarinov
    • 1
  1. 1.Automatics and Control DepartmentSouth Ural State UniversityChelyabinskRussia

Personalised recommendations