摘要
提出了一种基于傅里叶分析的支持向量机的电力系统短期负荷预测方法。利用离散傅里叶变换的方法将历史负荷数据分解为不同频域上的分量,将不同频域上的分量依据负荷的成因及其特性组合成四种不同性质的负荷分量,对上述各分量选择不同的预测模型,对于受温度等影响较大的负荷分量,构造支持向量机模型进行预测。实例计算表明该方法是可行和有效的。
A short-term load forecasting method in power system based on discrete fourier transform and support vector machines is proposed in this essay. Historical load data is decomposed into the series with different frequency characteristics by discrete fourier transform method. According to the features of load data,different frequency series are composed of four components. Different models are constructed to deal with the different components. Support vector machines is used to deal with the component which is influenced most by temperature. The feasibility and validity of the method in this paper is proved by practical example.
出处
《东北电力大学学报》
2006年第4期1-6,共6页
Journal of Northeast Electric Power University
关键词
傅里叶
支持向量机
负荷预测
频域
电力系统
Discrete fourier transform
Support Vector Machines
Load Forecasting
Frequency
Power system
