A combined model based on POA-VMD secondary decomposition and LSTM for ultra-short-term wind power forecasting

As the application of wind power expands, precise prediction of wind energy becomes essential for the effective plan and reliable functioning in the realm of the power system. Aiming to enhance wind power utilization efficiency and minimize error relating to ultra-short-term wind power forecasting, a novel model grounded in sliding time window, Pelican optimization algorithm-variational mode decomposition (POA-VMD) secondary decomposition, sample entropy calculation, sequence reconstruction, and long short-term memory (LSTM) prediction is introduced in this paper. First, in the training set, the sliding time window technique is employed to identify the optimal parameters for the forecasting algorithm, aiming to closely replicate the actual forecasting performance. Subsequently, the VMD algorithm is enhanced through optimization with the POA. This involves utilizing POA to dynamically ascertain the optimal parameters [k, α] for VMD, allowing for an adaptive decomposition of the raw wind power data sequence and effectively diminishing data noise. After calculating each modal's sample entropy, the modal with the highest sample entropy is further decomposed using POA-VMD. The decomposed sequence is predicted using LSTM to get the final prediction. The experiment ultimately demonstrated that the introduced model markedly improves the accuracy of forecasting. By adding POA-VMD secondary decomposition residuals, the prediction errors, as measured by mean absolute error, root mean square error, and mean absolute percentage error, are decreased by 52.03%, 30.34%, and 39.87%, respectively, and coefficient of determination (R2) is increased by 7.75%.