基于贝叶斯证据框架下WLS-SVM的短期负荷预测

Short-term load forecasting based on weighted least squares support vector machine within the Bayesian evidence framework

提出了一种基于贝叶斯证据框架下加权最小二乘支持向量机(Weighted Least Squares Support Vector Machine,WLS-SVM)的短期负荷预测模型和算法。在对历史负荷数据进行完预处理基础上,分析影响负荷变化的重要因素,然后选择最佳的输入数据作为LS-SVM训练模型的输入向量。通过贝叶斯证据三层推断寻找到模型的最佳参数:第一层推断确定LS-SVM的权向量w和偏置值b,第二层推断确定模型的超参数γ,第三层推断确定核函数的超参数σ。为了提高模型的鲁棒性,赋予了每个样本误差不同的权系数,建立了具有良好泛化性能的WLS-SVM回归模型,从而进一步提高了模型预测的精度。采用上述方法对黑龙江电网短期负荷进行了预测,结果证明了该方法具有良好的预测效果。

A short-term load forecasting model and algorithm based on the weighted least squares support vector machine within the bayesian evidence framework is proposed. On the basis ofpre-processing ofhistorical data, the author analyzes the important faetors of affecting the load change, and then selects the best input data as the input vector of LS-SVM training model. The optimal parameters of models can be found through three-layer bayesian evidence inference： The weight vector w and bias value b of LS-SVM can be determined in the first layer, and the hyper-parameter γ of the model can be inferred in the second layer, the hyper-parameter σ of the nuclear function fmally can be determined in the third layer. To improve the robustness of the model, WLS-SVM regression model with good generalization performance is established by giving a different weight coefficient to each sample error, which further improves the prediction accuracy of the model. Applying the proposed method to short-term load of Heilongjiang power system, results show the effectiveness of the method.