Short-term power generation forecasting study of PV based on PCA-CSA-Informer modeling
In order to improve the prediction accuracy for photovoltaic(PV)power generation,this paper proposes a new model for short-term PV power generation prediction that combines principal component analysis(PCA),channel spatial attention(CSA)and Informer.The multivariate time series of PV power generation is analyzed by Spearman correlation analysis,and the time series features are extracted by combining with PCA to construct the input dataset.At the same time,CSA mechanism module is introduced to extract the features of the time dimension and spatial dimension of the histori-cal data of PV power generation,which are then input into the Informer model for prediction.The pub-lic dataset of PV power plants with a resolution of 30 min is used for experimental validation and com-parative analysis,and the experimental results show that the prediction model proposed in this paper has mean absolute error of 0.0615,mean squared error of 0.0205,root mean squared error of 0.1435,and R2 of 0.9872 in four-step prediction,which are all superior to the other comparative models,and it is expected to provide PV short-term power generation prediction with better prediction accuracy.
photovoltaic power generation forecastshort-term power generationInformer modelprincipal component analysisdual channel attention mechanism
万方数据
维普
