A comparative study of optimal hybrid methods for wind power prediction in wind farm of Alberta, Canada

被引：37

作者：

Bigdeli, Nooshin ^{[1
]}

Afshar, Karim ^{[1
]}

Gazafroudi, Amin Shokri ^{[1
]}

Ramandi, Mostafa Yousefi ^{[1
]}

机构：

[1] Imam Khomeini Int Univ, EE Dept, Daneshgah Blv, Qazvin, Iran

来源：

RENEWABLE & SUSTAINABLE ENERGY REVIEWS | 2013年 / 27卷

关键词：

Genetic algorithm (GA); Imperialist competitive algorithm (ICA); Neural networks; Particle swarm optimization (PSO); Recurrence plots; Time series analysis; IMPERIALIST COMPETITIVE ALGORITHM; TCP/RED COMPUTER-NETWORKS; TIME-SERIES ANALYSIS; MARKET; MODEL; SPEED; CHAOS; PRICE;

D O I：

10.1016/j.rser.2013.06.022

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

In the recent years, by rapid growth of wind power generation in addition to its high penetration in power systems, the wind power prediction has been known as an important research issue. Wind power has a complicated dynamic for modeling and prediction. In this paper, different hybrid prediction models based on neural networks trained by various optimization approaches are examined to forecast the wind power time series from Alberta, Canada. At first, time series analysis is performed based on recurrence plots and correlation analysis to select the proper input sets for the forecasting models. Next, a comparative study is carried out among neural networks trained by imperialist competitive algorithm (ICA), genetic algorithm (GA), and particle swarm optimization approach. The simulation results are representative of the out-performance of ICA in tuning the neural network for wind power forecasting. (C) 2013 Elsevier Ltd. All rights reserved.

引用

页码：20 / 29

页数：10

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