Wind farm wind power prediction method based on CEEMDAN and DE optimized DNN neural network

Forecasting the generation of renewable energy power plants is increasingly becoming one of the basic technologies to ensure the safe and stable operation of power grids. In this paper, a new wind farm wind power prediction method based on CEEMDAN and DE optimized DNN neural network is proposed. Firstly, CEEMDAN is used to decompose the preliminary processed wind power historical data, and the LASSO method is used to eliminate the noise signal and re-fit. Then, the DE optimization algorithm is used to optimize the performance of the DNN neural network. Finally, the optimized DNN neural network is used to predict the short-term wind power of the wind farm. The CE-DE-RBF, CE-DE-BP, and CEDE-LSSVM models were used as comparison models. Predictive experiments were performed using real data from a wind power plant in northern China. The test results fully demonstrate that the proposed model has higher prediction accuracy in terms of three performance indicators than other comparison models.

[1]  Livio Casella,et al.  Wind speed reconstruction using a novel Multivariate Probabilistic method and Multiple Linear Regression: advantages compared to the single correlation approach , 2019, Journal of Wind Engineering and Industrial Aerodynamics.

[2]  Han Zhonghe,et al.  Research on LS-SVM Wind Speed Prediction Method Based on PSO , 2016 .

[3]  Jie Zhang,et al.  LSTM-EFG for wind power forecasting based on sequential correlation features , 2019, Future Gener. Comput. Syst..

[4]  Abheejeet Mohapatra,et al.  Repeated wavelet transform based ARIMA model for very short-term wind speed forecasting , 2019, Renewable Energy.

[5]  Patrick Flandrin,et al.  A complete ensemble empirical mode decomposition with adaptive noise , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[6]  Li Li,et al.  Sequence transfer correction algorithm for numerical weather prediction wind speed and its application in a wind power forecasting system , 2019, Applied Energy.

[7]  Asifullah Khan,et al.  Wind power prediction using deep neural network based meta regression and transfer learning , 2017, Appl. Soft Comput..

[8]  Zhenhao Tang,et al.  The multi-objective optimization of combustion system operations based on deep data-driven models , 2019, Energy.

[9]  Yuan Zhao,et al.  A novel hybrid model based on VMD-WT and PCA-BP-RBF neural network for short-term wind speed forecasting , 2019, Energy Conversion and Management.

[10]  Lei Wu,et al.  Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method , 2016 .

[11]  V. Ho-Huu,et al.  Optimal design of truss structures with frequency constraints using improved differential evolution algorithm based on an adaptive mutation scheme , 2016 .