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 journalArticle

6 Citations (Scopus)

Abstract

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
Volume87
DOIs
Publication statusPublished - 2016 Jun 1

Fingerprint

autoregressive moving average
Intelligent buildings
system identification
Identification (control systems)
excitation
noise measurement
control equipment
Time series
vibration
output
simulation

All Science Journal Classification (ASJC) codes

  • Instrumentation
  • Electrical and Electronic Engineering

Cite this

Kim, Yeesock ; Kim, Jung Mi ; Kim, Young Hoon ; Chong, Jowoon ; Park, Hyo Seon. / System identification of smart buildings under ambient excitations. In: Measurement: Journal of the International Measurement Confederation. 2016 ; Vol. 87. pp. 294-302.
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System identification of smart buildings under ambient excitations. / Kim, Yeesock; Kim, Jung Mi; Kim, Young Hoon; Chong, Jowoon; Park, Hyo Seon.

In: Measurement: Journal of the International Measurement Confederation, Vol. 87, 01.06.2016, p. 294-302.

Research output: Contribution to journalArticle

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