基于特征选择和组合模型的短期电力负荷预测

提出基于特征选择和组合模型的短期电力负荷预测方法。首先将特征向量按特点分为2类，分别使用斯皮尔曼相关系数、最大相关最小冗余算法进行选择，依据贝叶斯信息量准则确定最优特征向量维度。然后使用3个不同的核函数建立单核递归支持向量回归模型并完成预测。最后构建神经网络，进行实验分析。仿真结果表明所提方法具有较高的预测精度与鲁棒性。

Short-Term Load Forecasting Based on Feature Selection and Combination Model

A short-term load forecasting method based on feature selection and combination model is proposed. At first, the method divides the feature vectors into two sets according to the individual characteristics. Spearman rank-order correlation coefficient and max-relevance & min-redundancy algorithm are individually employed for selection. Bayesian information criterion is used to get the dimension of the optimal feature vector. And then, three different simple-kernel based support vector regression models are built using three kernel functions respectively and complete prediction. Finally, a neural network is set up for experimental analysis. The simulation results show that the proposed combination model has a great high forecasting accuracy and robustness.