基于密度峰值聚类及GRNN神经网络的光伏发电功率预测方法

PV POWER FORECASTING APPROACH BASED ON DENSITY PEAKS CLUSTERING AND GENERAL REGRESSION NEURAL NETWORK

提出一种基于密度峰值聚类和广义回归神经网络(general regression neural network,GRNN)的光伏发电功率短期预测方法。首先分析气象因素特征,计算统计指标构成天气特征向量;其次,为克服传统K均值聚类算法易陷入局部最优,需人为指定聚类中心等问题,采用密度峰值聚类算法对历史气象数据聚类识别;最终针对每一类簇构建GRNN预测模型。仿真结果表明,该文所述方法具有较高的预测精度,且在小样本环境下仍有良好的适用性。

A short-term forecasting method for PV power based on density peaks clustering and General Regression Neural Network is proposed.Firstly,analyzing the characteristics of weather factors,extracting statistical indicators to form the weather feature vector;then,using the density peaks algorithm to cluster the historical meteorological data to overcome the K-means algorithm,which is easy to fall into the local optimum and artificially specify the cluster center,etc.Simulation results show that the proposed method improves the accuracy of prediction and still has good applicability in small sample environments.