A PV power generation prediction based on the VMD-CNN-GRU model optimized by GTO
Accurate prediction of photovoltaic(PV)power is the key to ensuring the stable operation of the power system.To improve the accuracy of PV power prediction,by introducing an artificial gorilla troops optimizer(GTO)algorithm and variational mode decomposition(VMD),a combined prediction model(GTO-VMD-CNN-GRU)based on convolutional neural networks(CNN)and gated recurrent unit(GRU)neural networks was proposed.In this study,the quantitative meteorological feature extraction method based on Pearson's correlation coefficient was used to obtain feature importances for use as model inputs.To address the complexity and uncertainty of manual settings of VMD and model parameters,GTO was used to optimize the number of VMD and penalty factors to determine the optimal combination,and the main hyperparameters of the CNN-GRU model was optimized.By analyzing the predictions of PV output power,the results show that the CTO-VMD-CNN-GRU prediction model effectively improves the accuracy of PV output power predictions.By comparing the prediction effects with those of the other four methods,it was found that the proposed method performed the best in every error index.Therefore,the optimized model is more reliable.
gated recurrent unit(GRU)variational model decomposition(VMD)arithmetic optimization algorithmsphotovoltaic(PV)power prediction
NETL
NSTL
万方数据
