Ultra-short-term wind power forecasting based on long short-term memory network with modified honey badger algorithm

In recent years, research on ultra-short-term wind power forecasting models has gradually reached a bottleneck. Despite introducing numerous complex algorithms for wind power prediction, effective combinations have not been adequately explored. The useful information within wind power data has not been fully mined, and the selection of hyperparameters has not achieved an optimal combination, resulting in only a marginal improvement in forecasting accuracy. A novel wind power hybrid forecasting model, combining the modified honey badger algorithm, variational mode decomposition, and long short-term memory network, is proposed in this research. Taking the cosine similarity coefficient combined with root mean square error as the objective function, the modified honey badger algorithm is used to optimize the main parameters of the variational mode decomposition method, which improves the wind power data decomposition and denoising ability of variational mode decomposition and enables the deep mining of effective information in wind power data. Meanwhile, the modified honey badger algorithm is used to optimize the hyperparameters of long short-term memory, which solves the problem of difficult configuration of neural network parameters. Two different datasets with two different time intervals from two distinct Chinese wind farms were selected to validate the effectiveness of the proposed model. The experimental results indicate that the proposed model achieves superior forecasting performance metrics compared to other models across simulation experiments with various original datasets and prediction steps. The proposed model achieves high precision and good stability through the effective combination of multiple algorithms.