Photovoltaic power prediction method based on Resnet-VAE fusion model
Because the output of photovoltaic power generation is greatly affected by solar irradiation intensity,climate and other factors,its power curve characteristics often show nonlinear characteristics,and traditional prediction methods are difficult to fit.Therefore,a fusion model based on the combination of residual neural network(ResNet)and variational auto-encoder(VAE)is proposed.The variational auto-encoder is used to process the input data sequence.Based on the historical data of unit operation,the dimension reduction of model input attributes is realized by analyzing the correlation characteristics between different input attributes.The model uses the actual operation data of the unit for training to achieve accurate prediction of the unit power.
deep learningpower systemdata dimension reductionvariational autoencoderphotovoltaic power prediction
NETL
NSTL
万方数据
