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• The training time of Long Short-Term Memory is reduced by changing network structure. • A new hybrid model is proposed for wind speed forecasting. • Four experiments are designed to verify the performance and reliability of the model. Obtaining accurate wind speed forecast result plays a decisive role in ensuring the reliable operation of the power system integrated with large-scale wind power. Deep learning methods are increasingly being used to predict wind speed, which have relatively high prediction accuracy but more time-consuming training processes. The purposes of this study are further to improve the prediction accuracy of the wind speed and reduce the training time of the deep learning method. In this paper, a novel hybrid model, New Cell Update Long Short-Term Memory combined with Empirical Wavelet Transform, is proposed to increase the prediction accuracy in shorter training time. At first, the original wind speed sequence is preprocessed into a series of sub-sequence by the empirical wavelet decomposition. Then each sub-sequence is trained by New Cell Update Long Short-Term Memory which is proposed by this paper respectively and the sum of each sub-sequence is treated as the final prediction results. In order to verify the performance of the proposed model, different decomposition methods and different prediction methods are compared on the four actual wind speed prediction cases in the Inner Mongolia, China from prediction accuracy and training time. The results demonstrate that: (1) New Cell Update Long Short-Term Memory network has slightly higher prediction accuracy and shorter training time than Long Short-Term Memory network. (2) The prediction accuracy of the model is significantly improved after empirical wavelet decomposition. Therefore, New Cell Update Long Short-Term Memory network combined with empirical wavelet decomposition is a competitive wind speed prediction method compared to the existing state-of-the-art approach. [ABSTRACT FROM AUTHOR]

• Minimal Gated Memory Network is proposed to predict wind speed. • Quantile Regression Minimal Gated Memory Network is proposed to quantify uncertainty. • Feature selection and combination are introduced to construct optimal feature inputs. As a renewable and clean energy, wind energy plays an important role in easing the increasingly serious energy crisis. However, due to the strong volatility and randomness of wind speed, large-scale integration of wind energy is limited. Therefore, obtaining reliable high-quality wind speed prediction is of great importance for the planning and application of wind energy. The purpose of this study is to develop a hybrid model for short-term wind speed forecasting and quantifying its uncertainty. In this study, Minimal Gated Memory Network is proposed to reduce the training time without significantly decreasing the prediction accuracy. Furthermore, a new hybrid method combining Quantile Regression and Minimal Gated Memory Network is proposed to predict conditional quantile of wind speed. Afterwards, Kernel Density Estimation method is used to estimate wind speed probabilistic density function according to these conditional quantiles of wind speed. In order to make the model show better performance, Maximal Information Coefficient is used to select the feature variables while Genetic Algorithm is used to obtain optimal feature combinations. Finally, the performance of the proposed model is verified by seven state-of-the-art models through four cases in Inner Mongolia, China from five aspects: point prediction accuracy, interval prediction suitability, probability prediction comprehensive performance, forecast reliability and training time. The experimental results show that the proposed model is able to obtain point prediction results with high accuracy, suitable prediction interval and probability distribution function with strong reliability in a relatively short time on the prediction problems of wind speed. [ABSTRACT FROM AUTHOR]