Application of hybrid model based on double decomposition, error correction and deep learning in short-term wind speed prediction.

• Propose a novel short-term wind speed prediction model base on deep learning. • Employ the double decomposition method to process wind speed data. • Modify the forecasting errors in the error correction strategy. • Verify the validation of the proposed model by four real cases. As wind power accounts for an increasing proportion of the electricity market, the wind speed prediction plays a vital role in the stable operation of the power grid. However, owing to the stochastic nature of wind speed, predicting wind speeds accurately is difficult. Aims at this challenge, a new short-term wind speed prediction model based on double decomposition, error correction strategy and deep learning algorithm is proposed. The complete ensemble empirical mode decomposition with adaptive noise and variational mode decomposition are applied to decompose the original wind speed series and error series, respectively. The deep learning algorithm based on long short term memory neural network, is utilized to detect the long-term and short-term memory characteristics and build the suitable prediction model for each sub-series. In the four real forecasting cases, nine models were built to compare the performance of the proposed model. The experimental results show that the proposed model performs better than all other considered models without double decomposition, and the variational mode decomposition for error series can improve the effect of error correction strategy. [ABSTRACT FROM AUTHOR]