刘旭丽,莫毓昌,吴哲,严珂

• 1:华侨大学数学科学学院计算科学福建省高校重点实验室
• 2:中国计量大学信息工程学院

摘要(Abstract)：

太阳能是未来清洁能源的关键，由于各种气象因素的影响，光伏发电通常不稳定。准确预测光伏发电功率的方法已成为解决光伏发电规划和建模问题的重要工具，可以减轻电力系统的负面影响，提高系统的稳定性。提出了一种基于离散小波变换(discrete wavelet transform, DWT)、卷积神经网络(convolutional neural network, CNN)和长短期记忆神经网络(long short-term memory, LSTM)的新型域融合深度模型(DWT-CNN-LSTM),以准确地完成预测。提出的模型具有两个通道：原始通道和DWT通道。CNN分别从原始通道和DWT通道提取时域和频域特征，LSTM则用以挖掘具有长期依赖性的特征，从而形成具有长期依赖性的时域和频域的融合特征，可用于功率预测。

关键词(KeyWords)： 光伏发电;超短期预测;小波分解;卷积神经网络;长短期记忆神经网络

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金项目(61972165);; 数据科学福建省高校科技创新团队项目(MJK-2018-49);; 大数据分析与安全泉州市高层次人才团队项目(2017ZT012);; 福建省科技重大专项资助项目(2020HZ02014)

作者(Author): 刘旭丽,莫毓昌,吴哲,严珂

DOI: 10.13705/j.issn.1671-6841.2021292

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