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Language: English

Conflict of Interest: In relation to this article, we declare that there is no conflict of interest.

Publication history: Received February 3, 2010
Accepted April 24, 2010

This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Data-driven prediction model of indoor air quality in an underground space

Min Han Kim Yong Su Kim JungJin Lim Jeong Tai Kim¹ Su Whan Sung² ChangKyoo Yoo^†

Department of Environmental Science and Engineering, Center for Environmental Studies, Kyung Hee University, Seocheon-dong 1, Giheung-gu, Yongin-si, Gyeonggi-do 446-701, Korea ¹Department of Architectural Engineering, Kyung Hee University, Seocheon-dong 1, Giheung-gu, Yongin-si, Gyeonggi-do 446-701, Korea ²Department of Chemical Engineering, KyungPook National University, 1370 Sankyuk-dong, Buk-gu, Daegu 702-701, Korea

Korean Journal of Chemical Engineering, November 2010, 27(6), 1675-1680(6), 10.1007/s11814-010-0313-5

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Abstract

Several data-driven prediction methods based on multiple linear regression (MLR), neural network (NN), and recurrent neural network (RNN) for the indoor air quality in a subway station are developed and compared. The RNN model can predict the air pollutant concentrations at a platform of a subway station by adding the previous temporal information of the pollutants on yesterday to the model. To optimize the prediction model, the variable importance in the projection (VIP) of the partial least squares (PLS) is used to select key input variables as a preprocessing step. The prediction models are applied to a real indoor air quality dataset from telemonitoring systems data (TMS), which exhibits some nonlinear dynamic behaviors show that the selected key variables have strong influence on the prediction performances of the models. It demonstrates that the RNN model has the ability to model the nonlinear and dynamic system, and the predicted result of the RNN model gives better modeling performance and higher interpretability than other data-driven prediction models.

Keywords

Air Quality Prediction Nonlinear Modeling Recurrent Neural Networks (RNN) Predicted Model Partial Least Squares (PLS) Subway Station

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