丁明;刘志;毕锐;朱卫平;

• 1:教育部光伏系统工程研究中心(合肥工业大学)
• 2:江苏省电力公司电力科学研究院

摘要(Abstract)：

为提高非理想天气条件下的光伏功率预测精度,提出基于灰色系统校正-小波神经网络(wavelet neural network,WNN)的预测方法。首先以基于相似日算法的WNN进行逐时功率预测,并进行累加获得日累加功率。根据光伏出力历史数据,确定各广义天气类型的平均偏差比,并以平均偏差比进行平滑处理后的相邻日功率建立离散灰色系统模型(discrete gray model,DGM),进行日总功率预测并获得及其判断区间。最后以日总功率值判断区间为标准对累加功率值进行校正,得到校正后的各时段的预测值。算例结果验证了所提方法的有效性。

关键词(KeyWords)： 小波神经网络;灰色系统模型;相似日;相邻日;平均偏差比

Abstract:

Keywords:

基金项目(Foundation): “安徽省科技攻关项目(12010202036);; 2014江苏电力公司科技项目”的资助

作者(Author): 丁明;刘志;毕锐;朱卫平;

Email:

DOI: 10.13335/j.1000-3673.pst.2015.09.010

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