基于EMD-PSR-LSSVM的城市燃气管网短期负荷预测

城市燃气管网短期负荷预测对燃气调度系统的安全与稳定具有重要意义. 为了提高城市燃气管网短期负荷预测精度,建立了基于经验模态分解(EMD)-相空间重构(PSR)-最小二乘支持向量机(LSSVM)的组合预测模型. 首先,运用EMD算法把原始非线性时间序列分解为互不耦合的模态分量,并采用PSR算法确定LSSVM建模中各个分量的输入输出结构; 其次,运用PSO算法对LSSVM建模中的参数进行优化,使用训练好的LSSVM模型对各个IMF分量进行回归预测; 最后运用该组合模型对郑州市燃气管网负荷进行短期预测.结果表明:与LSSVM回归预测和BP神经网络预测模型相比,本文提出的组合模型的预测精度更高,是一种更为有效的城市燃气管网短期负荷预测方法.

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.