Gas Consumption Forecast Model in Steel Corporation Based on Grey RBF Neural Network

Gas consumption prediction is an important component of energy management in iron and steel corporation.To predict gas consumption in the steelmaking process,a grey radial basis function(RBF) neural network forecasting model was proposed by combining grey theory with RBF neural network.Grey accumulated generating operation was used for data preprocessing,which could reduce data randomness and enhance changes of data.RBF neural network was trained to predict these changes.Parameters of RBF network were modified on-line by the prediction error.The approach of BP neural network model was also investigated to provide a comparison with model.The results of simulation show that prediction accuracy is very high and mean square deviation is less than 2.02%.