| Summary: | Photovoltaic power generation as a green energy source is often used in power systems, but the volatility of PV output and randomness of the problem affect the stability of the power-grid power supply; so, for the problem of low prediction accuracy of photovoltaic power generation under different weather conditions, this paper proposes a Variational Mode Decomposition (VMD), combined with a Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) secondary decomposition method for the original signal decomposition, to reduce the signal volatility and reduce the complexity of feature mapping the PV data, followed by the use of a BiLSTM model to model the timing information of the decomposed IMF. Simultaneously, the Informer model predicts the components obtained from the secondary decomposition, and finally, the subsequence is reconstructed and superimposed to obtain the PV power prediction value. The results show that the RMSE and MAE of the proposed model are improved by up to 10.91% and 17.33% on the annual PV dataset, with high prediction accuracy and stability, which can effectively predict the ultra-short-term power of PV power plants.
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