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基于层次聚类法的EMD-ELM风电功率预测

The EMD-ELD Wind Power Prediction Based on Hierarchical Clustering Method

  • 摘要: 提出一种基于层次聚类法的EMD-ELM风电功率预测方法,用来解决目前风电站功率预测精度不够的问题。该方法利用层次聚类的聚合算法将天气情况相似的数据经行聚类,使用EMD方法来分解各组功率序列,可以得到相对平稳的数据分量,最后采用ELM模型对各分量经行预测并且重组。由于相似天气情况的数据特征更加的明显,所以经行聚类会使预测更加的准确。算例仿真表明,该模型与传统的预测模型相比有更高的准确度。

     

    Abstract: This paper put fooward one kind of EMD-ELD wind power prediction methods based on hierarchical clustering method for solving the problem that the wind power prediction is not accurate. Make the datas like weather information into cluster by aggregation algorithm of hierarchical clustering method. Use the EMD method tools decompose the power sequence of each group. Then the datas are relatively stable. At last use the ELM model to calculate and reorganize the components. The characteristics of weather datas isn't obvious, so the prediction through aggregation algorithm is more accurate. The example simulation shows that this model is more accurate than the traditional prediction model.

     

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