Hybrid multiscale wind speed forecasting based on variational mode decomposition

Abstract

Successful integration of wind energy in an electrical grid system requires prior information of the generated wind power from a turbine or a wind farm. That is a challenging task given the unpredictable, intermittent, and nonlinear nature of wind speed. We present a novel method for short-term wind speed prediction by using a hybrid approach based on variational mode decomposition (VMD) in conjunction with both linear and nonlinear prediction models. The VMD is a fully data-adaptive signal decomposition method that offers significant improvement over existing data-adaptive techniques, such as empirical mode decomposition, in terms of its sound mathematical framework and accuracy of its decomposition. In our work, VMD is used to decompose wind speed data into multiple intrinsic narrow band components to facilitate their forecasting/prediction. We report results from extensive experiments performed on a large wind speed data set obtained from multiple sites in Pakistan to demonstrate the efficacy of the proposed method.