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

被引：0
作者：
Mahdi Mir
Mahdi Shafieezadeh
Mohammad Amin Heidari
Noradin Ghadimi
机构：
[1] Ferdowsi University of Mashhad,Department of Electrical Engineering
[2] Yazd University,Young Researchers and Elite Club, Ardabil Branch
[3] Shiraz Electricity Distribution Company (SHEDC),undefined
[4] Islamic Azad University,undefined
来源：
Evolving Systems | 2020年 / 11卷
关键词：
Neural network; Wind power forecast; Hybrid forecast engine; Feature selection; EEMD;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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.
引用
收藏
页码：559 / 573
页数：14
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