Short-Term Wind Power Prediction Based on MVMD-AVOA-CNN-LSTM-AM - Xiqing Zang; Zehua Wang; Shixu Zhang; Mingsong Bai (International Transactions on Electrical Energy Systems)
Due to the intermittent and fluctuating nature of wind power generation, it is difficult to achieve the desired prediction accuracy for wind power prediction. For this reason, this paper proposes a combined prediction model based on the Pearson correlation coefficient method, multivariate variational mode decomposition (MVMD), African vultures optimization algorithm (AVOA) for leader–follower patterns, convolutional neural network (CNN), long short-term memory (LSTM), and attention mechanism (AM). Firstly, the Pearson correlation coefficient method is used to filter out the meteorological data with a strong relationship with wind power to establish the wind power prediction dataset; subsequently, MVMD is used to decompose the original data into multiple subsequences in order to handle the meteorological data better. Thereafter, the African vultures algorithm is used to optimize the hyperparameters of the CNN-LSTM algorithm, and the AM is added to increase the prediction effect, and the decomposed subsequences are predicted separately, and the predicted values of each subsequence are superimposed to obtain the final prediction value. Finally, the effectiveness of the model is verified using data from a wind farm in Shenyang. The results show that the MAE of the established MVMD-AVA-CNN-LSTM-AM model is 2.0467, and the MSE is 2.8329. Compared with other models, the prediction accuracy is significantly improved, and it had better generalization ability and robustness, and better generalization and robustness.