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

机译:加拿大艾伯塔省风电场中风电预测的最佳混合方法的比较研究

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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 (CA), 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.
机译:近年来,除了风力发电在电力系统中的高普及率之外,由于风力发电的快速增长,风力发电预测已被认为是重要的研究问题。风力发电具有复杂的动力学模型和预测。在本文中,研究了基于神经网络的不同混合预测模型,这些神经网络通过各种优化方法训练,以预测加拿大艾伯塔省的风电时间序列。首先,基于递归图和相关性分析执行时间序列分析,以为预测模型选择适当的输入集。接下来,在帝国主义竞争算法(ICA),遗传算法(CA)和粒子群优化方法训练的神经网络之间进行了比较研究。仿真结果代表了ICA在调整神经网络以进行风电预测方面的出色表现。

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