Application of hybrid forecast engine based intelligent algorithm and feature selection for wind signal prediction

• Published in: Evolving systems Vol. 11; no. 4; pp. 559 - 573
• Main Authors: Mir, Mahdi, Shafieezadeh, Mahdi, Heidari, Mohammad Amin, Ghadimi, Noradin
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-12-2020
• Subjects: Artificial Intelligence; Complex Systems; Complexity; Engineering; Original Paper; Wind power forecast; Feature selection; Neural network; Hybrid forecast engine; EEMD
• ISSN: 1868-6478; 1868-6486
• DOI: 10.1007/s12530-019-09271-y

This paper presents a new prediction model based on empirical mode decomposition, feature selection and hybrid forecast engine. The whole structure of proposed model is based on nonstationarity and non-convex nature of wind power signal. The hybrid forecast engine consists of three main stages as; empirical mode decomposition, an intelligent algorithm and back propagation neural network. All parameters of proposed neural network will be optimized by intelligent algorithm. Effectiveness of the proposed model is tested with real-world hourly data of wind farms in Spain and Texas. In order to demonstrate the validity of the proposed model, it is compared with several other wind speed and power forecast techniques. Obtained results confirm the validity of the developed approach.