A Combined Forecasting Model Based on a Modified Pelican Optimization Algorithm for Ultra-Short-Term Wind Speed

Ultra-short-term wind speed forecasting is crucial for ensuring the safe grid integration of wind energy and promoting the efficient utilization and sustainable development of renewable energy sources. However, due to the arbitrary, intermittent, and volatile nature of wind speed, achieving satisfactory forecasts is challenging. This paper proposes a combined forecasting model using a modified pelican optimization algorithm, variational mode decomposition, and long short-term memory. To address issues in the current combination model, such as poor optimization and convergence performance, the pelican optimization algorithm is improved by incorporating tent map-based population initialization, L & eacute;vy flight strategy, and classification optimization concepts. Additionally, to obtain the optimal parameter combination, the modified pelican optimization algorithm is used to optimize the parameters of variational mode decomposition and long short-term memory, further enhancing the model's predictive accuracy and stability. Wind speed data from a wind farm in China are used for prediction, and the proposed combined model is evaluated using six indicators. Compared to the best model among all compared models, the proposed model shows a 10.05% decrease in MAE, 4.62% decrease in RMSE, 17.43% decrease in MAPE, and a 0.22% increase in R2. The results demonstrate that the proposed model has better accuracy and stability, making it effective for wind speed prediction in wind farms.