System identification of smart buildings under ambient excitations

Yeesock Kim, Jung Mi Kim, Young Hoon Kim, Jowoon Chong, Hyo Seon Park

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


This paper proposes a nonlinear autoregressive moving average (NARMA) model for use in system identification (SI) of high performance smart buildings under ambient excitations. The NARMA model is implemented by including the cross terms of output signals to a linear autoregressive moving average (LARMA) time series model. To demonstrate the effectiveness of the proposed NARMA approach, a three-story building equipped with smart control devices is investigated under a variety of ambient excitations. To access the robustness of the proposed model, it is tested under various levels of measurement noises. It is demonstrated from the extensive simulations that the proposed NARMA model is effective in predicting the ambient vibration responses of the high performance smart buildings with severe measurement noises.

Original languageEnglish
Pages (from-to)294-302
Number of pages9
JournalMeasurement: Journal of the International Measurement Confederation
StatePublished - Jun 1 2016


  • Ambient excitations
  • Nonlinear autoregressive (AR) moving average (MA) model
  • Smart buildings
  • Smart control devices
  • System identification (SI)


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