Hourly Forecasting of SO2 Pollutant Concentration Using an Elman Neural Network

2006 ◽  
pp. 65-69
Author(s):  
U. Brunelli ◽  
V. Piazza ◽  
L. Pignato ◽  
F. Sorbello ◽  
S. Vitabile
Keyword(s):  
Neural Network ◽  
Pollutant Concentration ◽  
Elman Neural Network

Dynamic forecasting model for indoor pollutant concentration using recurrent neural network

2020 ◽  
pp. 1420326X2097473
Author(s):  
Lulu Hu ◽  
Na Fan ◽  
Jingguang Li ◽  
Yingwen Liu
Keyword(s):  
Neural Network ◽  
Air Pollution ◽  
Time Series ◽  
Pollutant Concentration ◽  
Forecasting Model ◽  
Direct Inference ◽  
Elman Neural Network ◽  
Indoor Pollutant ◽  
Dynamic Forecasting ◽  
Inference Strategy

Accurate and reliable indoor pollutant concentration prediction is essential to solve the time-lag problem of indoor air quality control systems. Thus, the representation of time in pollutant forecasting models is very important. One approach is to introduce an Elman neural network using a direct inference strategy into the time series forecast of indoor pollutant concentration. In this study, measurements of CO2 (ppm), total volatile organic compounds (mg/m3), particulate matter with a diameter smaller than 2.5 µm (PM2.5; µg/m3), the indoor dry bulb temperature (°C) and relative humidity (%) were carried out in a classroom at a middle school in Beijing, China. To identify air pollution antecedents, input selection was conducted based on correlation analysis. The results show that the information provided by the PM2.5 time series can better simulate the dynamic relationship between input and output data ([Formula: see text]= 0.963 and R2 = 0.928). In addition to the overall goodness of fit ([Formula: see text] = 0.982) of the CO2 time series, the peak and valley prediction capability of the model was evaluated using the relative peak error ( RPE) metric. Information from the valleys of the CO2 time series gives good results ([Formula: see text]). Therefore, a dynamic forecasting model with a direct inference strategy is a capable tool for identifying proper air pollution antecedents.

A Novel Forecast Method for Air-Conditioning Load of Public Building Considering Accumulated Temperature Effect

2015 ◽  
Vol 9 (1) ◽  
pp. 363-367
Author(s):  
Qingshan Xu ◽  
Xufang Wang ◽  
Chenxing Yang ◽  
Hong Zhu ◽  
Qingguo Yan
Keyword(s):  
Neural Network ◽  
Temperature Effect ◽  
Air Conditioning ◽  
Forecast Accuracy ◽  
Power Network ◽  
Forecast Method ◽  
Elman Neural Network ◽  
Public Building ◽  
Effective Accumulated Temperature ◽  
Accumulated Temperature

It has great significance to estimate the schedulable capacity of air-conditioning load of public building for participating the power network regulation by forecasting the air-conditioning load accurately. A novel forecast method considering the accumulated temperature effect is proposed in this paper based on Elman neural network. Firstly, the starting and ending date for forecast considering the accumulated temperature effect are determined by providing the five day sliding average thermometer algorithm which is usually adopted in aerology research. Then, the effective accumulated temperature of each day is calculated. Finally, take the effective accumulated temperature, temperature and humidity into consideration, the air-conditioning load of public building in the forecast day is acquired by Elman neural network. Simulated results show that the higher forecast accuracy can be achieved by considering the accumulated temperature effect.

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