Research on Short-term Prediction Model of Wave Energy Power Generation Based on CNN-BiLSTM-CBAM

The large fluctuation of wave energy will seriously affect the safe and stable operation of the power system when the wave energy power generation system is connected to the grid. Accurate prediction of wave energy power generation plays an important role in the real-time dispatching and control of the power system. In order to improve the prediction accuracy of wave energy power generation, taking the floating heave-buoy array(F-HBA) wave energy power generation device as the research object, and a wave energy power generation power prediction model is proposed based on CNN-BiLSTM-CBAM composite neural network. The model includes two sub modules, namely, the wave factor prediction module based on CNN-BiLSTM-CBAM composite neural network and the power conversion module based on F-HBA. First, the significant wave height and wave period are predicted, and then the predicted values of significant wave height and wave period are put into the power conversion model to obtain the predicted wave energy power generation. The prediction accuracy of wave power generation power prediction model based on CNN-BiLSTM-CBAM combined neural network is verified through actual simulation examples.