Multistep Wind Power Prediction Using Time-Varying Filtered Empirical Modal Decomposition and Improved Adaptive Sparrow Search Algorithm-Optimized Phase Space Reconstruction–Echo State Network

Accurate wind power prediction is vital for improving grid stability. In order to improve the accuracy of prediction, in this study, a hybrid model combining time-varying filtered empirical modal decomposition (TVFEMD), improved adaptive sparrow search algorithm (IASSA)-optimized phase space reconstruction (PSR) and echo state network (ESN) methods was proposed. First, data were decomposed into set subsequences by using TVFEMD. Next, PSR used construct corresponding matrix sequences, which then divided training sets, validation testing sets. Then, ESN subsequence prediction. Finally, predicted values all subseries determine final power. To enhance performance, terms discoverer position update strategy, follower population structure. IASSA employed synchronously optimize multiple parameters PSR-ESN. The results revealed that proposed has higher applicability than existing models.