基于PCA-SVM的电力系统短期负荷预测

Short-term Load Forecasting Based on PCA-SVM

针对基于支持向量机的电力系统短期负荷预测算法中,预测模型的精度和泛化能力易受样本集中输入变量的影响,利用主成分分析方法能有效地消除变量之间共线性的特点,通过提取样本集的主成分完成数据预处理,有效地压缩样本集的维数。根据East-Slovakia Power Distribution Company提供的电网运行数据进行了预测计算,证明此方法与标准支持向量机算法相比,可以降低样本集的维数,提高负荷预测精度。

According to the influence of input variables on the prediction precision and generalization capability of support vector machine algorithm, principal component analysis, which can eliminate the collinearity of variables, is used to accomplish data preprocessing by extracting characteristic information from training data set. Case studies using the history data offered by East-Slovakia Power Distribution Company show that PCA-SVM algorithm has good performances in reducing the dimension of the input-space as well as increasing forecasting accuracy.