Ultra-short-term Photovoltaic Power Prediction Based on VMD-LSTM-RVM Model

Abstract

Abstract Aiming at the randomness and obvious fluctuation of photovoltaic power, this paper proposes a method that combines Variational Modal Decomposition (VMD), Long Short-Term Memory (LSTM) network and Relevance Vector Machine (RVM) to achieve ultra-short-term photovoltaic power prediction. Firstly, the VMD decomposition technology is used to decompose the historical photovoltaic power sequence into different modes to reduce the non-stationarity of the data; then an LSTM prediction model is established for each mode, and the modal prediction values are reconstructed to obtain the power prediction value; in order to further improve the prediction accuracy of the model, the error sequence is modeled and predicted by RVM; finally, the prediction power value and the prediction error value are superimposed to obtain the final prediction result. Simulation results show that this method effectively improves the accuracy of photovoltaic power prediction.