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Ultra-Short-Term Multistep Prediction of Wind Power Based on Representative Unit Method

机译：基于代表单元法的风电超短期多步预测

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摘要

With the continuous expansion of wind power grid scale, wind power prediction is an important means to reduce the adverse impact of large-scale grid integration on power grid: the higher prediction accuracy, the better safety, and economy of grid operation. The existing research shows that the quality of input sample data directly affects the accuracy of wind power prediction. By the analysis of measured power data in wind farms, this paper proposes an ultra-short-term multistep prediction model of wind power based on representative unit method, which can fully excavate data information and select reasonable data samples. It uses the similarity measure of time series in data mining, spectral clustering, and correlation coefficient to select the representative units. The least squares support vector machine (LSSVM) model is used as a prediction model for outputs of the representative units. The power of the whole wind farm is obtained by statistical upscaling method. And the number of representative units has a certain impact on prediction accuracy. The case study shows that this method can effectively improve the prediction accuracy, and it can be used as pretreatment method of data. It has a wide range of adaptability.
机译：随着风电规模的不断扩大，风电预测是减少大规模电网整合对电网不利影响的重要手段：预测精度越高，电网安全性越好，经济性越好。现有研究表明，输入样本数据的质量直接影响风力发电预测的准确性。通过对风电场实测数据的分析，提出了一种基于代表单元法的风电超短期多步预测模型，可以充分挖掘数据信息，选择合理的数据样本。它在数据挖掘，频谱聚类和相关系数中使用时间序列的相似性度量来选择代表单位。最小二乘支持向量机（LSSVM）模型用作代表单元输出的预测模型。整个风电场的功率通过统计放大方法获得。代表单位数量对预测精度有一定影响。实例研究表明，该方法可以有效地提高预测精度，可以作为数据的预处理方法。它具有广泛的适应性。

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  • 来源
    《Mathematical Problems in Engineering》 |2018年第14期|1936565.1-1936565.11|共11页
  • 作者

    Yang Mao; Liu Lei; Cui Yang; Su Xin;

  • 作者单位

    Northeast Elect Power Univ, Sch Elect Engn, Jilin 132012, Jilin, Peoples R China;

    Northeast Elect Power Univ, Sch Elect Engn, Jilin 132012, Jilin, Peoples R China;

    Northeast Elect Power Univ, Sch Elect Engn, Jilin 132012, Jilin, Peoples R China;

    Northeast Elect Power Univ, Sch Elect Engn, Jilin 132012, Jilin, Peoples R China;

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