Wind speed prediction model using ensemble empirical mode decomposition, least squares support vector machine and long short-term memory

Abstract

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Due to the randomness and intermittency of wind, accurate and reliable wind speed prediction is of great importance to the safe and stable operation of power grid. In this paper, a novel hybrid wind speed forecasting model based on EEMD (Ensemble Empirical Mode Decomposition), LSSVM (Least Squares Support Vector Machine), and LSTM (Long Short-Term Memory) is proposed, aiming at enhancing the forecasting accuracy of wind speed. The original data series is firstly processed by EEMD and SE into a series of components with different frequencies. Subsequently, a combined mechanism composed of LSSVM and LSTM is presented to train and predict the high-frequency and low-frequency sequences, respectively. Finally, the predicted values of all the data sequences are superimposed to obtain the ultimate wind speed forecasting results. In order to respectively illustrate the superiority of data feature processing and combined prediction mechanism in the proposed model, two experiments are performed on the two wind speed datasets. In accordance with the four performance metrics of the forecasting results, the EEMD-LSTM-LSSVM model obtains a higher accuracy in wind speed prediction task.

Keywords

• wind speed forecasting
• long short-term memory
• least squares support vector machine
• ensemble empirical mode decomposition
• sample entropy