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Short-Term Wind Speed Forecasting Study and Its Application Using a Hybrid Model Optimized by Cuckoo Search

机译：杜鹃搜索优化混合模型的短期风速预测研究及其应用

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摘要

The support vector regression (SVR) and neural network (NN) are both new tools from the artificial intelligence field, which have been successfully exploited to solve various problems especially for time series forecasting. However, traditional SVR and NN cannot accurately describe intricate time series with the characteristics of high volatility, nonstationarity, and nonlinearity, such as wind speed and electricity price time series. This study proposes an ensemble approach on the basis of 5-3 Hanning filter (5-3H) and wavelet denoising (WD) techniques, in conjunction with artificial intelligence optimization based SVR and NN model. So as to confirm the validity of the proposed model, two applicative case studies are conducted in terms of wind speed series from Gansu Province in China and electricity price from New South Wales in Australia. The computational results reveal that cuckoo search (CS) outperforms both PSO and GA with respect to convergence and global searching capacity, and the proposed CS-based hybrid model is effective and feasible in generating more reliable and skillful forecasts.
机译：支持向量回归（SVR）和神经网络（NN）都是人工智能领域的新工具，已经成功地用于解决各种问题，尤其是时间序列预测。但是，传统的SVR和NN无法准确地描述具有高波动性，非平稳性和非线性（例如风速和电价时间序列）特征的复杂时间序列。这项研究基于5-3 Hanning滤波器（5-3H）和小波去噪（WD）技术，结合基于SVR和NN模型的人工智能优化，提出了一种集成方法。为了证实该模型的有效性，针对中国甘肃省的风速序列和澳大利亚新南威尔士州的电价进行了两个应用案例研究。计算结果表明，杜鹃搜索（CS）在收敛性和全局搜索能力方面均优于PSO和GA，并且基于CS的混合模型在生成更可靠，更熟练的预测方面是有效且可行的。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第12期|608597.1-608597.18|共18页
  • 作者

    Chen Xuejun; Jin Shiqiang; Qin Shanshan; Li Laping;

  • 作者单位

    Chinese Meteorol Adm, Inst Arid Meteorol, Key Open Lab Arid Climat Change & Disaster Reduct, Key Lab Arid Climat Change & Reducing Disaster Ga, Lanzhou 730020, Peoples R China|Gansu Meteorol Serv Ctr, Lanzhou 730020, Gansu, Peoples R China;

    Lanzhou Univ, Sch Math & Stat, Lanzhou 730000, Gansu, Peoples R China;

    Lanzhou Univ, Res Sch Arid Environm & Climate Change, MOE Key Lab Western Chinas Environm Syst, Lanzhou 730000, Peoples R China|Chinese Acad Sci, Sci Informat Ctr Resources & Environm, Natl Sci Lib, Lanzhou Branch, Lanzhou 730000, Peoples R China;

    Datong Meteorol Bur Shanxi Prov, Datong 037010, Peoples R China;

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