OFFSHORE WIND POWER FORECASTING BASED ON WIND SPEED-POWER COMBINATION DECOMPOSITON

To enhance prediction accuracy，this study proposes a novel deep learning model that leverages data combination decomposition and Bayesian optimization. Initially，utilizing improved complete ensemble empirical mode decomposition with adaptive noise and variable modal decomposition optimized by the sparrow search algorithm，offshore wind speed data and historical power data are subjected to composite decomposition. Subsequently， the modal components obtained from composite decomposition are reconstructed using fuzzy entropy analysis to simplify the model. Finally，Bayesian-optimized long short-term memory neural networks are employed to forecast each power component，and the results of these components are aggregated to obtain the prediction of offshore wind power generation. Experimental data from offshore wind farms indicate that the proposed method efficiently mitigates the interference resulting from significant fluctuations in the original data，resulting in higher accuracy for both single-step and multi-step predictions compared to a conventional single-model approach utilizing offshore wind speed or power data decomposition. © 2024 Science Press. All rights reserved.