基于密度峰值聚类优化的光伏发电功率预测Power prediction of photovoltaic power generation based on clustering optimization of density peaks
王帅,杜欣慧,姚宏民
摘要(Abstract)：
密度峰值聚类算法具有收敛速度快、鲁棒性强、无需人为确定最佳聚类数等特点,具备较好的应用前景。为提高光伏功率预测的精度,提出一种将密度峰值聚类算法应用于短期光伏功率预测的方法,并进行了必要优化。该方法首先通过类间距离优化增强气象数据的可分性;然后利用密度峰值聚类对其进行无标签归类,通过灰色关联度匹配出与待预测日相关度最高的类别;最后将其作为Elman神经网络的训练样本,得到预测结果。Matlab仿真结果表明,该方法能够明显提高气象数据的聚类效果,并有效提高光伏功率的短期预测精度。
关键词(KeyWords)： 密度峰值聚类;光伏发电;灰色关联度;相似日匹配;Elman神经网络;短期功率预测
基金项目(Foundation):
作者(Author): 王帅,杜欣慧,姚宏民
DOI: 10.16652/j.issn.1004-373x.2018.20.034
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