Tabu Search algorithm based general regression neural network for long term wind speed predictions

Abstract

Accurate prediction of wind speed is needed as the wind power directly depends upon the wind speed. Because of the complex non-stationary and nonlinear characteristics of wind speed, it is difficult to achieve good prediction accuracy. Compared to the prediction models that use single algorithms, hybrid models always have higher accuracy. The decomposition algorithm called Empirical Mode Decomposition (EMD) is combined with the optimization algorithm named Tabu Search (TS) and General Regression Neural Network (GRNN) to achieve high precision and is proposed in this study. The performance of the proposed approach is evaluated using wind speed datasets of different cities in India. The detail of the proposed model is given as follows: EMD (Empirical Mode Decomposition) decomposes the original datasets of wind speed into intrinsic mode functions (IMFs). A partial autocorrelation function determines the number of neurons in the input layer of GRNN. An intelligent algorithm namely Tabu Search is used to optimize the neural networks globally. The proposed model has better prediction accuracy in long term wind speed forecasting.