Short-Term Wind Power Prediction Model Based on Improved BBO and Optimized BP Neural Network
To improve the recognition ability of the prediction model in dealing with complex patterns and nonlin-ear features in wind power time series data,a new prediction model is proposed in this study.Signal processing is performed by the improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise algorithm,and then the initial weights of the Back Propagation Neural Network are optimized according to the improved BBO algorithm to further improve the accuracy and stability of short-term wind power prediction.Practical appli-cation cases show that compared with other optimization algorithms,the performance of the proposed model on MAE,RMSE and MAPE is improved by 43.21%,37.98%and 36.84%on average,respectively,showing higher prediction accuracy.Simulation results validate the effect and obvious advantages of the proposed method in the field of short-term wind power prediction.
short-term wind power predictionComplete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)Back Propagation Neural Network(BPNN)Biogeography-Based Optimization(BBO)
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