Short-term Load Forecasting of Natural Gas Pipeline Based on Combination Model
Grey correlation analysis(GRA)is used to determine the main control factors affecting the short-term load of natural gas pipeline.Nonlinear change strategy is applied to improve the inertia weight and acceleration factor of particle swarm optimization(PSO)algorithm.The optimization algorithm is used to find the hyperparameters suitable for the long short-term memory network(LSTM)model,and a combination model of GRA-IPSO-LSTM is formed.It is compared with other models to verify its accuracy and reliability.The results show,according to the size of grey correlation degree,the factors that have little influence on daily load can be deleted step by step,the complexity of the subsequent prediction model can be reduced.iteration speed,convergence accuracy and optimization quality are improved in IPSO algorithm.The limitation of manual selection of LSTM model hyperparameters is reduced.The MAPE and RMSE of the combined model are sharply reduced,theR2 and the prediction accuracy are improved.It proves that the combined model can be used to accurately predict the short-term load of natural gas pipelines.
NETL
NSTL
万方数据
