PV power prediction based on AO-VMD-RF-Informer

Abstract

Due to the strong volatility of PV power, PV grid-connected may have an impact on the safe and stable operation of the power system, so accurate prediction of PV power is of great significance to the operation and maintenance of the power system. In order to improve the prediction accuracy of photovoltaic power, an ultra-short-term photovoltaic power prediction method was studied by combining the Aquila Optimizer (AO) algorithm, the Variational Mode Decomposition (VMD), the Random Forest (RF) and the Informer prediction model. Firstly, the VMD parameters are optimized by AO to reduce the adverse effects of human-set parameters on the prediction accuracy; the optimized VMD is used to decompose the original PV power series into multiple sub-sequences to reduce the volatility and complexity of the original power series; then, the RF feature selection method is used to screen out the meteorological features of strong relevance for each sub-sequence to further reduce the feature dimensions and the model runtime and ensure the effectiveness of the input features. Finally, the Informer model is used to deeply mine the potential time series features of each subsequence for prediction, and the predicted values of each subsequence are superimposed and reconstructed to obtain the final prediction results. The simulation results show that the method in this paper has high prediction accuracy, and compared with the original Informer, the MAE is reduced by 49.14% and the RMSE is reduced by 47.64%.