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。
Because power system load forecasting was a uncertain, nonlinear, dynamic and complicated system, it was difficult to describe such a nonlinear characteristics of this system by traditional methods, so the load forecasting could not be accurately forecasted. The authors presented a novel load forecasting method in which an improved Support Vector Machines (SVM) algorithm based on time sequence was applied and the principle of Structural Risk Minimization (SRM) was embedded into the SVM, therefore, on the basis of learning by fewer samples the presented method could conduct fast and accurate load forecasting with other samples fitting load forecasting. The presented method was more generalized and its dependence on experience was weakened. In the time sequence the trend component and periodical component were considered to make the load forecasting model more coincident with the features of power loads. Applying the presented method to actual load forecasting, the comparison among the forecasted results and the true shows that the presented method is feasible and effective.
出处
《电网技术》
EI
CSCD
北大核心
2004年第19期38-41,共4页
Power System Technology
