杨锡运,马文兵,彭琰,孟令卓超,王晨旭,马骏超

• 1:华北电力大学控制与计算机工程学院
• 2:国网浙江省电力有限公司电力科学研究院

摘要(Abstract)：

分布式光伏电站在电力系统中的渗透率逐年升高，为保障电网安全稳定运行，提出一种基于组合神经网络的分布式光伏超短期功率预测方法。首先利用一维卷积神经网络（1DCNN）与长短时记忆（LSTM）神经网络构建1DCNN&1DCNN-LSTM组合神经网络模型，获取多位置数值天气预报（NWP)信息与历史功率信息；然后利用组合神经网络模型进行空间相关性光伏功率预测与时间序列预测，并在组合神经网络模型中加入全连接神经网络（FCNN），利用全连接神经网络对2种预测结果进行学习与权重分配，实现了分布式光伏发电功率的超短期预测。采用河北某光伏电站实测数据进行验证，验证结果表明，该方法能够有效提高分布式光伏预测精度，具有一定的实用价值。

关键词(KeyWords)： 分布式光伏;超短期功率预测;LSTM;1DCNN;深度学习

Abstract:

Keywords:

基金项目(Foundation): 国网浙江省电力有限公司科技项目（5211DS220009）~~

作者(Author): 杨锡运,马文兵,彭琰,孟令卓超,王晨旭,马骏超

DOI: 10.19666/j.rlfd.202212235

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