FCM-UW-ADAGRU-based Ultra-short-term PV Prediction Considering Relevant Information of Similar Days
The present work studied and proposed a fuzzy C-means clustering and uncertainty-weighted adaptive gating u-nit neural network (FCM-UW-ADAGRU)model,aiming at the prediction of day-ahead minute-level PV output.First the historical daily weather was classified through FCM by using 5 statistical indexes of historical power data,i.e.coordinated mean,geometric mean,coefficient of variation,kurtosis,and skewness,as the clustering characteristics.Then different data distributions from similar day samples of the same weather type were identified by distribution recognition module,and the relevant information can be mined from all similar daily data through the distribution matching module,in order to deal with possible unknown meteorological information about the future.Finally balance prediction error and related infor-mation error based on uncertainty weighting (UW)were employed to improve model training accuracy.In a case study,the above method was compared to currently prevailing methods,demonstrating its higher accuracy and robustness,and verifying the effectiveness of the proposed model.
adaptive gating unit neural networkfuzzy C-means clusteringphotovoltaic ultra-short-term predictionsimilar day
万方数据
维普
