Wind speed forecasting method based on deep learning strategy using empirical wavelet transform, long short term memory neural network and Elman neural network

Abstract

The wind speed forecasting plays an important role in the planning, controlling and monitoring of the intelligent wind power systems. Since the wind speed signal is stochastic and intermittent, it is difficult to achieve their satisfactory prediction. In the study, a novel hybrid deep-learning wind speed prediction model, which combines the empirical wavelet transformation and two kinds of recurrent neural network, is proposed. In the proposed new model, the empirical wavelet transformation is adopted to decompose the raw wind speed data into several sub-layers. The long short term memory neural network, a deep learning algorithm based method, is utilized to predict the low-frequency wind speed sub-layers. The Elman neural network, a mainstream recurrent neural network, is built to predict the high-frequency sub-layers. In the executed forecasting experiments, eleven different forecasting models are included to validate the real prediction performance of the proposed model. The experimental results indicate that the proposed model has satisfactory performance in the high-precision wind speed prediction.