An efficient evolutionary approach to parameter identification in a building thermal model

Zhenyu YANG, Xiaoli LI, Chris P. BOWERS, Thorsten SCHNIER, Ke TANG, Xin YAO

Research output: Journal PublicationsJournal Article (refereed)peer-review

24 Citations (Scopus)


Thermal models of buildings are often used to identify energy savings within a building. Given that a significant proportion of that energy is typically used to maintain building temperature, establishing the optimal control of the buildings thermal system is important. This requires an understanding of the thermal dynamics of the building, which is often obtained from physical thermal models. However, these models require detailed building parameters to be specified and these can often be difficult to determine. In this paper, we propose an evolutionary approach to parameter identification for thermal models that are formulated as an optimization task. A state-of-the-art evolutionary algorithm, i.e., SaNSDE, has been developed. A fitness function is defined, which quantifies the difference between the energy-consumption time-series data that are derived from the identified parameters and that given by simulation with a set of predetermined target model parameters. In comparison with a conventional genetic algorithm, fast evolutionary programming, and two state-of-the-art evolutionary algorithms, our experimental results show that the proposed SaNSDE+ has significantly improved both the solution quality and the convergence speed, suggesting this is an effective tool for parameter identification for simulated building thermal models. © 2012 IEEE.
Original languageEnglish
Article number6105579
Pages (from-to)957-969
Number of pages13
JournalIEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews
Issue number6
Early online date15 Dec 2011
Publication statusPublished - Nov 2012
Externally publishedYes

Bibliographical note

This work was supported in part by the National Natural Science Foundation of China under Grant U0835002, Grant 61028009, and Grant 61175065, by the National Natural Science Foundation of Anhui Province under Grant 1108085J16, by the Engineering and Physical Sciences Research Council under Grant EP/F062567/1 on “Advanced Analysis of Building Energy Performance using Computational Intelligence Approaches,” and by the European Union 7th Framework Program under Grant 247619. This paper was recommended by Associate Editor N. O. Attoh-Okine.


  • Building thermal model
  • differential evolution (DE)
  • evolutionary optimization
  • parameter identification


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