基于EOF-DBSCAN-GRU的分布式光伏集群出力预测方法研究

RESEARCH ON DISTRIBUTED PV CLUSTER POWER OUTPUT FORECASTING METHOD BASED ON EOF-DBSCAN-GRU

提出一种基于EOF-DBSCAN划分集群的改进统计升尺度的光伏集群出力预测建模方法。针对传统统计升尺度方法子集群中光伏电站出力特性不一致问题,通过皮尔逊相关系数和经验正交函数(EOF)优化特征空间,再根据密度聚类模型(DBSCAN)对区域内光伏电站划分集群,从而增强光伏电站聚类后集群出力特性的一致性。针对待预测日权重系数时间序列动态特性的提取、预测问题,提出一种基于动态时间规整(DTW)的相似日选取算法。最后利用循环神经网络(GRU)模型进行光伏电站出力预测。实验表明该集群预测方法的平均误差百分数(MAPE)约为6.33%,均方根误差(RMSE)约为13.93 kW,均方误差(MSE)为194.25 kW,通过实际光伏电站数据证明了所提方法的准确性和有效性。

An improved statistical scale-up modeling method for PV cluster output forecasting based on empirical orthogonal function(EOF)and density-base spatial clustering of applications with noise(DBSCAN)is proposed.To solve the problems of inconsistent output characteristics of PV power stations in the subgroup of traditional statistical upscaling method,Pearson correlation coefficient and EOF were used to optimize the feature space,and then the PV power stations in the region were divided into clusters according to DBSCAN model,so as to enhance the consistency of the output characteristics of PV power stations after clustering.Aiming at the problems of extracting and predicting the dynamic characteristics of time series of weight coefficients of days to be forecasted,a similar day selection algorithm based on dynamic time warping(DTW)was proposed.Finally,the gate recurrent unit(GRU)neural network model is built to predict the power output of PV power stations.Experimental results show that the mean absolute percentage error(MAPE),root mean square error(RMSE)and mean square error(MSE)of the cluster forecasting method are about 6.33%,13.93 and 194.25 kW.The effectiveness and accuracy of the proposed method are verified by the measured data of actual PV power stations.