基于聚类和支持向量回归的短期负荷预测方法

提出了一种基于自组织特征映射(SOFM)的聚类分析和支持向量回归(SVR)的电力系统短期负荷预测方法。该方法首先利用自组织特征映射网络,通过无监督学习策略,对训练样本集进行聚类分析,将其分为若干相似子类;再针对每一子类构造一个支持向量回归(SVR)模型,以对应子类的样本集训练SVR模型。由于聚类后的每一子类的样本具有相似性,同时子类样本数较少,因此,该方法能够缩短训练时间,提高预测精度。基于某电网提供的历史负荷数据进行的不同方法对比实验说明了该方法的有效性。

Short Term Load Forecasting Using Clustering based Support Vector Regression

This paper proposes a short term load forecasting(STLF) method by using clustering based support vector regression model.The proposed method is first based on self-organizing feature map(SOFM) that can discover the similar input data and cluster them into several subsets in an unsupervised strategy.Then,several SVR models are constructed in corresponding to the subsets;each SVR model is trained with its corresponding subset.Due to the similarity in training data and the reduction of the amount of training data for each SVR model,the proposed method can forecast with more accurate results while enhancing the training speed.Comparison of simulation results of different methods based on the historical load data proves the feasibility of this model.