Wind Power Forecasting by a New Hybrid Forecast Engine Composed of GA/EPSO-Based Mutual Information and Group Method of Data Handling (GMDH)

a really tough and complicated issue. In this paper, a successful method is proposed for predicting the wind power productions which is based on the self-organized neural networks called Group Method of Data Handling (GMDH). By analyzing and discovering the hidden relationships between the inputs, the GMDH-based neural network intelligently presents the optimal model and predicts the output variable. Patterns used in this study are based on the two methods of artificial intelligence and information theory. At first, the effective variables are selected based on Mutual Information (MI) technique and the mixed particle swarm and genetic algorithm and after that the proposed forecast engine is used. In contrast to the mutual correlation method, in the proposed cross-entropy-based approach of this paper, non-linear relations between the variables are considered and the selection of effective variables in the forecasting of wind power in which nonlinear fluctuations and trends are observed are chosen more precisely and more accurately. In order to evaluate the ability, speed and accuracy of the proposed framework, real-world data of Sotavento wind farm in the Spain were used. The results of the study indicate that the proposed technique has a higher speed and accuracy than other methods.