NDLI: Prediction of Indoor Air Quality Using Artificial Neural Networks

Content Provider	IEEE Xplore Digital Library
Author	Hui Xie Fei Ma Qingyuan Bai
Copyright Year	2009
Abstract	This paper described an application of artificial neural networks (ANNs) to predict the indoor air quality (IAQ). Six indoor air pollutants and three indoor comfort variables were used as input variables to the networks. An occupant symptom metric (PIAQ) was used as the measure of indoor air quality, and employed as the output variable.Pollutant concentration, comfort variable, and PIAQ data were obtained from previous studies. Feed-forward networks that employed back-propagation algorithm with momentum term and variable learning rate were used in ANN modeling.Among constructed networks, the best prediction performance was observed in a two-hidden-layered network with the high correlation coefficient and low root mean square error for the test set. Meanwhile, the constructed networks had a better performance than the multiple linear regression analysis. The results showed that the ANN approach can be applied successfully in predicting indoor air quality.
Starting Page	414
Ending Page	418
File Size	959765
Page Count	5
File Format	PDF
ISBN	9780769537368
DOI	10.1109/ICNC.2009.502
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	indoor air quality Input variables Linear regression Artificial neural networks Predictive models Root mean square artificial neural networks prediction Air pollution Pollution measurement Performance analysis Feedforward systems Testing
Content Type	Text
Resource Type	Article

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