王昕;黄柯;郑益慧;李立学;邵凤鹏;贾立凯;徐清山;

• 1:上海交通大学电工与电子技术中心
• 2:国网重庆市电力公司北碚供电分公司
• 3:国网吉林省电力有限公司延边供电公司
• 4:国网吉林省电力有限公司

摘要(Abstract)：

随着光伏发电大容量地并入电网,其输出的随机性必将对大电网安全稳定运行造成影响,为此建立了一种变权重的光伏短期组合预测模型,首先通过主成分分析法(principal component analysis,PCA)将影响光伏出力的多重线性因素进行压缩、提取以简化模型输入变量的维数,然后将提取的第一主成分结合灰色关联度来筛选相似日样本,接着将样本分别带入最小二乘支持向量机、改进BP网络2种单一模型进行2次预测。第1次预测作为相似日预测,用来训练权重系数,训练方法是萤火虫算法优化的广义回归神经网络;第2次预测是待预测日的预测。仿真结果验证了所提模型的有效性。

关键词(KeyWords)： 主成分分析法;灰色关联度;萤火虫算法;广义回归神经网络

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金重点项目(61533012);; 上海市自然科学基金(14ZR1421800)~~

作者(Author): 王昕;黄柯;郑益慧;李立学;邵凤鹏;贾立凯;徐清山;

Email:

DOI: 10.13335/j.1000-3673.pst.2016.0943

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