An integrated short-term load forecasting approach for urban gas pipeline network based on EMD, PSR and LSSVM.

Urban gas pipeline network short-term load forecasting is important for the security and stability of gas distribution dispatching system. In order to improve the forecast precision, this study adopts an integrated model of empirical mode decomposition, phase space reconstruction and least squares support vector machine, i.e., EMD-PSR-LSSVM, for urban gas pipeline network short-term load forecasting. Firstly, EMD is used to decompose the original nonlinear time series into several uncoupling intrinsic mode functions. Then, PSR is used to make the selection of LSSVM input/output-layer units. Furthermore, particle swarm algorithm is used to optimize the model parameters and train LSSVM with temporal sequence samples, the trained LSSVM will be used for regression forecasting in advanced. Finally, the original loading data of Zhengzhou is adopted as example for empirical analysis. Results indicate that the EMD-PSR-LSSVM model has a higher outcome as compared to BP neural network and LSSVM regression, which has demonstrated the proposed integrated model is efficient and consistent. [ABSTRACT FROM AUTHOR]