System identification of smart buildings under ambient excitations

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

doi:10.1016/j.measurement.2016.02.028

System identification of smart buildings under ambient excitations

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

Department of Architecture and Architectural Engineering

Research output: Contribution to journal › Article › peer-review

9 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 language	English
Pages (from-to)	294-302
Number of pages	9
Journal	Measurement: Journal of the International Measurement Confederation
Volume	87
DOIs	https://doi.org/10.1016/j.measurement.2016.02.028
Publication status	Published - 2016 Jun 1

Bibliographical note

Funding Information:
This work was supported by a National Research Foundation of Korea (NRF) grant funded by the South Korea government (MEST) (No. 2011-0018360 ).

All Science Journal Classification (ASJC) codes

Instrumentation
Electrical and Electronic Engineering

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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.",

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