Application of Neural Network Model Based on RF-WOA-VMD-BiGRU-Attention in Wave Prediction
The extremely complex sea state data of offshore wind farms makes the input parameters used for wave height prediction extremely unstable,and screening out key information and improving the quality of input parameters can greatly improve the accuracy of wave height prediction.Based on the offshore data of the wind farm in Laoting Bodhi Island for nearly one year,a wave prediction model based on random forest(RF),whale optimization algorithm(WOA),variational mode decomposition(VMD)and bidirectional gated recurrent unit(BiGRU)was constructed.The model used random forest to screen input variables such as environmental charac-teristics to effectively reduce data redundancy,and then adaptively determined the optimal parameters and adaptively decomposed the original sequence based on the WOA-VMD model to improve data quality and eliminate the interference of data noise.In addition,a BiGRU algorithm based on attention mechanism optimization was proposed for wave prediction,and the attention mechanism of random forest would assign different weights to the hidden layer state of BiGRU to strengthen the influence of key information.The experimental results show that compared with other models,the model has higher input quality,higher prediction accuracy and more accurate fitting,which is of great significance for the prediction of wind farm waves.
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