Generation power prediction method of distributed new energy system based on Wavelet Neural Network

The existing generation power prediction methods of distributed new energy system have the problems of low prediction accuracy and poor efficiency. Therefore, a prediction research based on wavelet neural network is proposed. Firstly, the wavelet transform technology is used to decompose the time series data of power generation to extract the power information covering different frequencies. Then, the feature data obtained by wavelet transform is used as the input vector of neural network, and a wavelet neural network model which combines the advantages of wavelet transform and the strong learning ability of neural network is deeply trained. Finally, the prediction model is used to realize the high-precision prediction of new energy power generation. The experimental results show that the method has excellent convergence performance and prediction error, and the maximum error is only 0.12, which provides a strong technical support for the stable operation and efficient management of the distributed new energy system.