基于贝叶斯理论和在线学习支持向量机的短期负荷预测

该文将贝叶斯理论用于短期负荷预测(STLF)中输入特征的自适应选取。该理论将所有能够获得的信息，包括样本信息和先验知识结合在一起加以利用，不但避免了过拟合问题，而且简化了预测模型。文中同时建立了基于支持向量机(SVM)在线学习的短期负荷预测模型。在充分利用SVM解的稀疏性并结合KKT条件的基础上，以递增和递减算法可直接得到新的回归函数而无需重新训练，从而提高了一般SVM方法进行负荷预测的计算速度。多个实际系统的预测算例表明了该方法在预测精度和预测速度方面的有效性。

BASED ON BAYESIAN THEORY AND ONLINE LEARNING SVM FOR SHORT TERM LOAD FORECASTING

The paper adopts Bayesian theory to input feature selection for short term load forecasting (STLF). It makes use of the information from both samples and prior knowledge. In this way, not only can the over-fitting problem be effectively solved but also the model of forecasting can be simplified. Simultaneously, an online learning support vector machine (SVM) method for short-term load forecasting model is presented here. The method comprises incremental algorithm and decrement algorithm, which efficiently updates a trained regression function whenever a sample is added to or removed from the training set. So it is favorable for applications like online learning or leave-one-out cross-validation. The practical examples show that online learning support vector machine with input feature selection based on Bayesian theory outperforms other methods in both forecasting accuracy and computing speed.