Sensors, Vol. 23, Pages 8003: Revealing Long-Term Indoor Air Quality Prediction: An Intelligent Informer-Based Approach

Sensors, Vol. 23, Pages 8003: Revealing Long-Term Indoor Air Quality Prediction: An Intelligent Informer-Based Approach Sensors doi: 10.3390/s23188003 Authors: Hui Long Jueling Luo Yalu Zhang Shijie Li Si Xie Haodong Ma Haonan Zhang Indoor air pollution is an urgent issue, posing a significant threat to the health of indoor workers and residents. Individuals engaged in indoor occupations typically spend an average of around 21 h per day in enclosed spaces, while residents spend approximately 13 h indoors on average. Accurately predicting indoor air quality is crucial for the well-being of indoor workers and frequent home dwellers. Despite the development of numerous methods for indoor air quality prediction, the task remains challenging, especially under constraints of limited air quality data collection points. To address this issue, we propose a neural network capable of capturing time dependencies and correlations among data indicators, which integrates the informer model with a data-correlation feature extractor based on MLP. In the experiments of this study, we employ the Informer model to predict indoor air quality in an industrial park in Changsha, Hunan Province, China. The model utilizes indoor and outdoor temperature, humidity, and outdoor particulate matter (PM) values to forecast future indoor particle levels. Experimental results demonstrate the superiority of the Informer model over other methods for both long-term and short-term indoor air q...
Source: Sensors - Category: Biotechnology Authors: Tags: Communication Source Type: research