TY - GEN
T1 - Data-driven prediction model of indoor air quality by the preprocessed recurrent neural networks
AU - Kim, Min Han
AU - Kim, Yong Su
AU - Sung, Su Whan
AU - Yoo, Chang Kyoo
PY - 2009
Y1 - 2009
N2 - In this study, data-driven prediction methods based on recurrent neural networks (RNN) for indoor air quality in a subway station are developed. The RNN can predict the air pollutant concentration of PM10 and PM 2.5 at a platform of a subway station by using the previous information of NO, NO2, NOX, CO, CO2, Temperature, humidity, and PM10 and PM2.5 on yesterday. For comparison, the other prediction models such as neural networks (NN) and multiple regression model are used. To optimize the prediction model, the variable importance in the projection (VIP) of the PLS is used to select key input variables as a preprocessing step. Experimental result shows that the selected key variables have positive influence on the prediction performance. The predicted result of RNN model gives better modeling performance and higher interpretability than other data-driven prediction modeling methods.
AB - In this study, data-driven prediction methods based on recurrent neural networks (RNN) for indoor air quality in a subway station are developed. The RNN can predict the air pollutant concentration of PM10 and PM 2.5 at a platform of a subway station by using the previous information of NO, NO2, NOX, CO, CO2, Temperature, humidity, and PM10 and PM2.5 on yesterday. For comparison, the other prediction models such as neural networks (NN) and multiple regression model are used. To optimize the prediction model, the variable importance in the projection (VIP) of the PLS is used to select key input variables as a preprocessing step. Experimental result shows that the selected key variables have positive influence on the prediction performance. The predicted result of RNN model gives better modeling performance and higher interpretability than other data-driven prediction modeling methods.
KW - Air quality prediction
KW - Nonlinear modeling
KW - Partial least squares (PLS)
KW - Predicted model
KW - Recurrent neural networks (RNN)
UR - http://www.scopus.com/inward/record.url?scp=77951141165&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:77951141165
SN - 9784907764333
T3 - ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings
SP - 1688
EP - 1692
BT - ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings
T2 - ICROS-SICE International Joint Conference 2009, ICCAS-SICE 2009
Y2 - 18 August 2009 through 21 August 2009
ER -