An effective hybrid wind power forecasting model based on "decomposition-reconstruction-ensemble" strategy and wind resource matching

The random and fluctuating nature of wind energy brings tremendous challenges and disturbances to the security operation of wind power systems, accurate wind power prediction can effectively reduce these negative impacts. To this end, this paper proposes a hybrid wind power prediction model based on the "decomposition-reconstruction-ensemble" strategy, which consists of four main components, namely decomposition, reconstruction, prediction, and ensemble. Specifically, the original wind power series is decomposed into several sub-modes and reconstructed by frequency by the sample entropy(SE)-optimized variational modal decomposition(VMD) algorithm, subsequently, the Pearson correlation coefficients between the wind speed time series and the reconstructed components of wind power are calculated to divide the wind power series into trend and fluctuation components. Then both the two components are sequentially predicted using the temporal convolutional network(TCN) model. The final predicted value is obtained from the set of predicted results for each component. The wind power data from two wind farms in Hami, Xinjiang are adopted as examples for empirical study, and the results show that the IVMD-R-TCN model proposed in this paper performs significantly better than the benchmark model, which illustrates the predictive validity of the proposed model and is an effective tool for wind power forecasting.