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題名:	Short-Term PM2.5 Forecasting Using Exponential Smoothing Method: A Comparative Analysis
作者:	蔡子傑 Tsai, Tzu-Chieh Mahajan, Sachit* Chen, Ling-Jyh
貢獻者:	資科系
關鍵詞:	Internet of Things; air quality forecast; PM2.5; Smart Cities
日期:	2018-09
上傳時間:	2019-10-30 10:26:53 (UTC+8)
摘要:	Air pollution is a global problem and can be perceived as a modern-day curse. One way of dealing with it is by finding economical ways to monitor and forecast air quality. Accurately monitoring and forecasting fine particulate matter (PM2.5) concentrations is a challenging prediction task but Internet of Things (IoT) can help in developing economical and agile ways to design such systems. In this paper, we use a historical data-based approach to perform PM2.5 forecasting. A forecasting method is developed which uses exponential smoothing with drift. Experiments and evaluation were performed using the real-time PM2.5 data obtained from large scale deployment of IoT devices in Taichung region in Taiwan. We used the data from 132 monitoring stations to evaluate our model’s performance. A comparison of prediction accuracy and computation time between the proposed model and three widely used forecasting models was done. The results suggest that our method can perform PM2.5 forecast for 132 monitoring stations with error as low as 0.16 μ g/ m3 and also with an acceptable computation time of 30 s. Further evaluation was done by forecasting PM2.5 for next 3 h. The results show that 90 % of the monitoring stations have error under 1.5 μ g/ m3 which is significantly low.
關聯:	Sensors, Vol.18, No.10, pp.3223:1-15
資料類型:	article
DOI 連結:	https://doi.org/10.3390/s18103223
DOI:	10.3390/s18103223
顯示於類別:	[資訊科學系] 期刊論文

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