基于多分辨率SVM回归估计的短期负荷预测

针对短期负荷预测支持向量机（SVM）方法的局部逼近能力和泛化能力进行研究，将多分辨率支持向量机（M—SVM）用于短期负荷预测中节点负荷预测曲线的回归估计。该理论在保持曲线总体逼近能力的同时提高了局部区域的逼近能力。文中根据短期负荷预测的具体特点，设计了负荷预测数学模型，采用96条回归曲线进行日负荷的曲线预测，并在该模型的基础上采用实际数据进行验证，分析了这种回归模型的泛化能力。实验结果表明M-SVM模型在预测精度和预测速度方面具有优良的特性。

Short Term Load Forecasting Based on Multi-resolution SVM Regression

The multi-resolution SVM (M-SVM) is adopted for the regressive estimation of the short-term load forecasting curve after a study of the approaching and extending capabilities of SVM, in which several kernels of different scales of SVM can be used simultaneously to approximate to the target function and improve the effectiveness of generalization and approximation in the local area model. Additionally, the data set is arranged into 96 regression functions for every time spot in a whole day, respectively. The performance of the model proposed is evaluated through a comparison with other algorithms such as the traditional SVM and artificial neural network (ANN) methods. Test results of an actual power system show that it has better local approximation and generalization capabilities when appropriate numbers and parameters of the kernels of SVM are chosen.