基于负荷混沌特性和最小二乘支持向量机的短期负荷预测

Short-Term Load Forecasting Based on Chaotic Characteristic of Loads and Least Squares Support Vector Machines

以负荷时间序列的混沌特性为基础,结合混沌时间序列的相空间重构理论和支持向量机的回归理论建立了一种基于负荷混沌特性和最小二乘支持向量机的短期负荷预测模型。首先将原始负荷数据进行相空间重构,形成相点序列,然后选择与当前相点最邻近的相点作为此负荷预测模型的训练样本,经过训练寻求决策函数后就可以求出包含预测点的相点,最后还原此相点得出预测值。通过与BP神经网络的预测结果进行比较,证明了该模型在短期负荷预测中的有效性。

Based on the chaotic characteristic of time series of power loads and combining the phase space reconstruction theory of chaotic time series and regression theory of supporting vector machines （SVM）, a short-term load forecasting model based on chaotic characteristic of loads and least squares SVM （LS-SVM） is built. At first, the phase space reconstruction of original load data is performed to form phase point series; then the phase points most adjacent to current phase points are chosen as the training samples for the proposed load forecasting model; after the decision function is found by training, the phase points involving the forecasted point can be solved; finally, reverting this phase point, the forecasted load value is obtained. Comparing the forecasting resluts by the proposed method with those from BP neural network method, the advantage and effectiveness of the proposed model in short-term load forecasting is proved.