Short-term PV Power Prediction Model Based on VMD-CNN-IBiLSTM

Abstract Because PV power’s generation is unpredictable, so timely and accurate prediction of PV power has great practical significance and research value for daily grid dispatch and power system security. The combined forecast model presented in our study is dependent on variational modal decomposition-convolutional neural network-improved bidirectional long short-term memory neural network (VMD-CNN-IBiLSTM) with PV power as the research object. The model uses VMD to decompose PV power sequences, thus reducing the non-smoothness of the sequences; CNN to extract the essential features of the information; BiLSTM with peephole connections to learn the forward and backward features of the temporal data; and genetic algorithm to enhance the model’s variables. For validation, actual data from the Australian DKASC testbed are used. According to the experimental findings, our study’s combined model is more precise than the classical BP neural network, SVM model, and LSTM model at making predictions.