Short-term wind power prediction model based on PCA-GA-LSTM neural network

In order to improve the prediction accuracy of short-term wind power, a PCA-GA-LSTM short-term wind power combination prediction algorithm based on NWP is proposed. First, principal component analysis is used to reduce the dimension and denoise NWP sample data, and then genetic algorithm is used to train and optimize the model parameters of short-term memory artificial neural network (LSTM), such as learning rate, number of hidden layer nodes, regularization coefficient, etc. Finally, NWP historical data after dimension reduction is used as learning samples to train the neural network, and real-time information is processed to obtain prediction results. The results show that the prediction accuracy based on PCA-GA-LSTM neural network model is good, which has certain reference value for short-term wind power prediction.