Short-Term Wind Power Prediction Based on Hybrid Neural Network and Chaotic Shark Smell Optimization

By the quick growth of wind power generation in the world, this clean energy becomes an important green electrical source in many countries. However, volatile and non-dispatchable nature of this energy source motivates researchers to find accurate and robust methods to predict its future values. Because of nonlinear and complex behaviors of this signal, more efficient wind power forecast methods are still demanded. In this paper a new forecasting engine based on Neural Network (NN) and a novel Chaotic Shark Smell Optimization (CSSO) algorithm is proposed. Choosing optimal number of nodes for the hidden layer can enhance the efficiency of the NN's training performance. Accordingly, a new meta-heuristic algorithm is presented in this paper which is based on shark abilities in nature, for optimizing the number of hidden nodes pertaining to the NN. Effectiveness of the proposed forecasting strategy is tested on two real-world case studies for predicting wind power. The obtained results demonstrate the capability of the proposed technique to cope with the variability and intermittency of wind power time series for providing accurate predictions of its future values.