With the continuous consumption of fossil energy and environmental pollution, natural gas as a clean energy has received more and more attention. How to accurately predict the future of natural gas load has a vital significance. A long-term natural gas load forecasting model based on GBP is established to solve the problem of natural gas load forecasting. First, the method of correlation index is used to optimize the 20 indicators and then the optimized index number is 16; second, combining the advantages of gray neural network (BP) in fitting time series and particle swarm optimization in optimization parameters, a long-term load forecasting model of natural gas based on PSO-BP is established; finally, in order to verify the validity of the model, taking the natural gas load sample from 2005 to 2015 in Anhui Province as an example, the BP and GBP alone prediction models are compared with this model. The results show that compared with BP and GBP alone, the PSO-GBP prediction model improved the mean absolute deviation and mean absolute percentage error values by 0.065 and 0.03485 and 6.67944 and 3.62817, respectively, and increased the calculation time by 0.00726 and 0.00378 s.
Prediction of long-term gas load based on particle swarm optimization and gray neural network model
