A Semi-blind Model with Parameter Identification for Building Temperature Estimation

Xing Luo*, Xu Zhu, Eng Gee Lim, Yi Huang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

An accurate thermal model for building enables the heating system (HS) to work efficiently as well as save energy. Thermal modelling often requires physical parameters of the building, which are difficult to be accurately determined. The aim of this work is to develop an optimal thermal model for better understanding of thermal dynamics with the goal of using this to estimate temperature variation in a few hours ahead within building. Based on the characteristics of thermal motion, a conventional physics-based (PB) model for building temperature estimation is introduced first. Afterwards, in order to refine the model and improve the actual performance, we propose an innovative semi-blind (SB) model based on data-driven approaches. Additionally, the methodologies including self-adaptive algorithms (SAAs) and grey prediction technique (GPT) have been applied in dealing with the integrated parameters estimation (IPE) process to ensure the practicability of the implemented model. The proposed model schema is validated by testing in a laboratory. The results indicate that the proposed approach achieves much higher accuracy in estimating temperature variation than the conventional PB model, with only limited knowledge of the building characteristics. The root mean square deviation (RMSD) of SB model and PB model are 0.18 and 0.43, respectively. According to the results, it can be concluded that the proposed SB model is able to appropriately estimate the internal temperature values and great improvement has been achieved comparing with the original thermal model.

Original languageEnglish
Pages (from-to)105-116
Number of pages12
JournalCognitive Computation
Volume10
Issue number1
DOIs
Publication statusPublished - 1 Feb 2018

Keywords

  • Parameter estimation
  • Physics-based (PB) model
  • Semi-blind (SB) model
  • Thermal modelling

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