Repositorio UVigo

Forecasting SO 2 Pollution Incidents by means of Elman Artificial Neural Networks and ARIMA Models

Investigo Repository

Forecasting SO 2 Pollution Incidents by means of Elman Artificial Neural Networks and ARIMA Models

Sánchez, Antonio Bernardo; Ordóñez, Celestino; Lasheras, Fernando Sánchez; de Cos Juez, Francisco Javier; Roca Pardiñas, Javier
 
DATE : 2013
UNIVERSAL IDENTIFIER : http://hdl.handle.net/11093/1213
UNESCO SUBJECT : 3318.01 Minería del Carbón ; 3308.01 Control de la Contaminación Atmosférica ; 1209 Estadística
DOCUMENT TYPE : article

ABSTRACT :

An SO2 emission episode at coal-fired power station occurs when the series of bihourly average of SO2 concentration, taken at 5-minute intervals, is greater than a specific value. Advance prediction of these episodes of pollution is very important for companies generating electricity by burning coal since it allows them to take appropriate preventive measures. In order to forecast SO2 pollution episodes, three different methods were tested: Elman neural networks, autoregressive integrated moving average (ARIMA) models, and a hybrid method combining both. The three methods were applied to a time series of SO2 concentrations registered in a control station in the vicinity of a coal-fired power station. The results obtained showed a better performance of the hybrid method over the Elman networks and the ARIMA models. The best prediction was obtained 115 minutes in advance by the hybrid model.

Show full item record



Files in this item

2013 Universidade de Vigo, Todos los derechos reservados
Calidad So9001