【摘要】 由于负荷预测是不确定、非线性、动态开放性的复杂大系统,传统方法往往难以准确地描述这种复杂的非线性特征,因而无法准确进行负荷预测。作者提出了基于一种基于时间序列的支持向量机(SVM)的负荷预测方法。SVM 方法采用结构风险最小化原则(SRM),能够在对小样本学习的基础上,对其它样本进行快速、准确的拟合预测,具有更好的泛化性能和精度,减少了对经验的依赖。时间序列考虑了趋势分量和周期分量,使负荷预测模型更加符合电力负荷特性。将该方法用于实际负荷预测中。和真实值的比较说明所提出的负荷预测方法是可行和有效的。更多还原

【Abstract】 Because power system load forecasting was uncertain, nonlinear, dynamic and complicated system, it wa difficult to describe such a nonlinear characteristics of thi system by traditional methods, so the load forecasting coul not be accurately forecasted. The authors presented a nove load forecasting method in which an improved Support Vecto Machines (SVM) algorithm based on time sequence wa applied and the principle of Structural Risk Minimizatio (SRM) was embedded into the SVM, therefore, on the basis o learning by fewer samples the presented method could conduc fast and accurate load forecasting with other samples fittin load forecasting. The presented method was more generalized and its dependence on experience was weakened. In the tim sequence the trend component and periodical component wer considered to make the load forecasting model more coinciden with the features of power loads. Applying the presente method to actual load forecasting, the comparison among th forecasted results and the true shows that the presented metho is feasible and effective.更多还原