System identification of smart buildings under ambient excitations

• Published in: Measurement : journal of the International Measurement Confederation Vol. 87; pp. 294 - 302
• Main Authors: Kim, Yeesock, Kim, JungMi, Kim, Young Hoon, Chong, Jowoon, Park, Hyo Seon
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.06.2016
• Subjects: Ambient excitations; Nonlinear autoregressive (AR) moving average (MA) model; Smart buildings; Smart control devices; System identification (SI); System identification (SI); Smart buildings; Ambient excitations; Nonlinear autoregressive (AR) moving average (MA) model; Smart control devices
• ISSN: 0263-2241; 1873-412X
• DOI: 10.1016/j.measurement.2016.02.028

•The first attempt to explore ambient response identification of smart buildings.•High performances in modeling smart buildings under ambient conditions.•Better performance than the linear model in predicting ambient responses. 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.