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An Experimental Investigation of FNN Model for Wind Speed Forecasting Using EEMD and CS

机译：基于EEMD和CS的风速预报FNN模型的实验研究。

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摘要

With depletion of traditional energy and increasing environmental problems, wind energy, as an alternative renewable energy, has drawn more and more attention internationally. Meanwhile, wind is plentiful, clean, and environmentally friendly; moreover, its speed is a very important piece of information needed in the operations and planning of the wind power system. Therefore, choosing an effective forecasting model with good performance plays a quite significant role in wind power system. A hybrid CS-EEMD-FNN model is firstly proposed in this paper for multistep ahead prediction of wind speed, in which EEMD is employed as a data-cleaning method that aims to remove the high frequency noise embedded in the wind speed series. CS optimization algorithm is used to select the best parameters in the FNN model. In order to evaluate the effectiveness and performance of the proposed hybrid model, three other short-term wind speed forecasting models, namely, FNN model, EEMD-FNN model, and CS-FNN model, are carried out to forecast wind speed using data measured at a typical site in Shandong wind farm, China, over three seasons in 2011. Experimental results demonstrate that the developed hybrid CS-EEMD-FNN model outperforms other models with more accuracy, which is suitable to wind speed forecasting in this area.
机译：随着传统能源的枯竭和环境问题的日益严重，风能作为一种可替代的可再生能源，在国际上越来越受到关注。同时，风能充足，清洁且环保。此外，其速度是风力发电系统的运行和规划中非常重要的信息。因此，选择具有良好性能的有效预测模型在风电系统中起着相当重要的作用。本文首先提出了一种混合CS-EEMD-FNN模型，用于风速的多步超前预测，其中EEMD被用作数据清除方法，旨在消除嵌入在风速序列中的高频噪声。 CS优化算法用于在FNN模型中选择最佳参数。为了评估所提出的混合模型的有效性和性能，还使用其他三个短期风速预测模型，即FNN模型，EEMD-FNN模型和CS-FNN模型，使用测得的数据进行风速预测。在中国山东风电场的一个典型站点，在2011年超过了三个季节。实验结果表明，所开发的CS-EEMD-FNN混合模型在准确性上优于其他模型，适合于该地区的风速预测。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第7期|464153.1-464153.13|共13页
  • 作者

    Wang Jianzhou; Jiang Haiyan; Han Bohui; Zhou Qingping;

  • 作者单位

    Dongbei Univ Finance & Econ, Sch Stat, Dalian 116025, Peoples R China.;

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

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

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

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