基于多嵌入维数的时用水量LSSVM组合预测

为解决供水系统调度所需混沌时用水量高精度预测等问题,将最小二乘支持向量机(LSSVM)组合预测模型应用到城市时用水量预测中。在分析不同嵌入维数和预测方法对模型预测精度影响程度的基础上,提出了基于多嵌入维数的LSSVM组合预测模型。采用互信息法和G-P方法求取多个嵌入维数,并建立了不同相空间模型,通过LSSVM算法对上述多个预测模型进行了组合预测,既综合了各不同嵌入维数各预测方法下的信息,又对单一模型下的预测偏差进行了融合,以有效地提高预测精度;最后在某地进行了时用水量序列的仿真实验。研究结果表明,该模型预测精度平均误差小于2%,明显优于各单一模型的预测结果,证实了该组合模型的有效性和实用性。

Combined prediction of urban hourly water consumption using LSSVM based on multi-dimension embedding phase space

In order to improve the accuracy of chaotic hour consumption prediction in urban water supply system,a combined forecasting model for urban hourly water consumption using least squares support vector machine（LSSVM） was investigated.After the analysis of different effects on chaotic system forecast accuracy by various forecast methods and parameters of phase space reconstruction,a combined forecasting model for urban hourly water consumption using LSSVM based on multi-dimension embedding phase space was established.The different embedding dimensions were estimated by combining mutual information method and G-P algorithm.Combined forecasting models were solved by LSSVM which can take advantage of all information in all dimension embeddings and forecast methods.The predictive bias under the single model was merged.In this way,the forecast accuracy was improved.The simulation results of hourly water consumption forecast in aplace shows that the forecast error is blow 2% and better than other forecasting results in single model.This proves the effectiveness and practicability of the approach.