Multi-step wind speed forecasting based on secondary decomposition algorithm and optimized back propagation neural network

Improve the reliability of wind speed forecasting is a crucial task in wind power generation. Due to the stochastic and noise nature of wind, a preprocessing step is beneficial for wind speed series to get clean data. The decomposition technique is reported as the critical preprocessor to transform the unstable wind speed data into several regular components. This study proposes a hybrid forecasting system, which combines secondary decomposition algorithm and optimized back propagation (BP) neural network. For the decomposition part, the variational mode decomposition (VMD) is firstly used to extract the low-frequency part from the original wind data. Then the symplectic geometry mode decomposition (SGMD) decomposes the rest high-frequency part into clean and separate components. The BP algorithm is optimized by the differential evolution (DE) as the predictor in this study. Empirical studies with different comparison models are conducted on real wind speed data. The results affirm the competitive strength of the proposed combination strategy. And the proposed two-stage decomposition technique is applicable for nonlinear wind speed analysis. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

Improving the reliability of wind speed forecasting in wind power generation is crucial, and this study introduces a hybrid forecasting system using secondary decomposition and neural network, demonstrating the competitive strength of this combination strategy.