基于MIV-PCA的超短期风电功率预测模型优化

为解决基于动态神经网络的超短期风电功率预测方法中预测模型输入变量多、模型复杂等问题,文中将平均影响值(MIV)和主元分析(PCA)方法相结合,对预测模型进行了优化。MIV方法表征了输入变量对输出的影响程度,可筛选出对预测输出具有最大影响的输入变量,简化预测模型,但变量的信息利用率不高。PCA法从剩余的输入变量中提取出主元,通过增加少量的主元变量提高信息利用率,弥补MIV方法的不足。数据分析及实验结果表明,通过MIV和PCA法优化的预测模型的输入变量能在获得较高的累计贡献率的同时降低模型复杂度,保留原系统的重要信息,并降低模型引入噪声的风险,使得风电功率预测精度得到显著提高。

Optimization of ultra-short-term wind power predicting model based on MIV-PCA

In order to solve the problems such as variable redundancy and model complexity in ultra-short-term wind power prediction based on dynamic neural network (DNN), a novel method is proposed by combine the mean impact value (MIV) and principal component analysis (PCA) to optimize the predicting model constructed by DNN method. MIV method calculates the influencing degree from the input variables to the output and obtain the most important input variables to simplify the predicting model. However, its information utilization is low. PCA method extracts the principal components from the rest of the input variables. The information utilization can be greatly improved by adding a small number of principal components to make up for the deficiency of MIV method. It is verified by the data analysis and experiment that the optimized predicting model can assure the high contribution of the input variables and reduce the model complexity, which preserves the important information of the original system greatly, reduces the risk of introducing noise to the model, and makes the predicting result being improved significantly.