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

Abstract

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

Hybrid Hunter–Prey Optimization with Deep Learning-Based Fintech for Predicting Financial Crises in the Economy and Society

“…The SSA is applied in this study to adjust the hyperparameter values of the GARN model. SSA is a metaheuristic optimization technique based on the foraging and anti-predation behaviors of sparrows [21]. In this work, discoverers, followers, and watchers are three different strategies of individuals in SSA.…”