一种基于CNN-LSTM的改进CGAN光伏短期出力场景生成方法

秦卫民; 唐昊; 任曼曼; 梁肖; 王涛; 陈韬

doi:10.19912/j.0254-0096.tynxb.2023-2045

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太阳能学报 ›› 2025, Vol. 46 ›› Issue (4) : 263-272. DOI: 10.19912/j.0254-0096.tynxb.2023-2045

一种基于CNN-LSTM的改进CGAN光伏短期出力场景生成方法

秦卫民¹, 唐昊¹, 任曼曼², 梁肖², 王涛¹, 陈韬³

作者信息 +

IMPROVED CGAN PHOTOVOLTAIC SHORT-TERM OUTPUT SCENARIO GENERATION METHOD BASED ON CNN-LSTM

Qin Weimin¹, Tang Hao¹, Ren Manman², Liang Xiao², Wang Tao¹, Chen Tao³

Author information +

文章历史 +

摘要

该文考虑新能源机组出力数据的时空特征,设计一种带有卷积神经网络和长短期记忆网络的判别器网络结构,并使用推土机（EM）距离作为判别器的损失函数,提出一种基于条件对抗生成网络的新能源短期场景生成方法。该方法让模型中的判别器与生成器进行对抗并不断优化,使生成器网络更加准确地提取到条件值及噪声分布与样本分布之间的映射关系,从而更好地生成新能源机组出力场景。该文使用开源的光伏出力数据对模型进行验证和测试,相对于传统的生成式对抗网络方法,所提模型能更加准确地生成契合历史数据特征的新能源出力场景集。

Abstract

This article considers the spatiotemporal characteristics of output data of new energy units and designs a discriminator network structure with convolutional neural networks and long short-term memory networks. The EM distance is used as the loss function of the discriminator, and a new energy short-term scene generation method based on conditional adversarial generation network is proposed. This method trains the generator network through game theory between the generator and discriminator in the model, enabling it to more accurately extract the mapping relationship between conditional values, noise distribution, and sample distribution, thereby better generating new energy unit output scenarios. This article uses open-source photovoltaic output data to validate and test the model, and compares it with the generative adversarial network scene generation method based on fully connected structures. The results show that the proposed model can more accurately generate short-term output scene sets that match the characteristics of real samples.

导出引用

秦卫民, 唐昊, 任曼曼, 梁肖, 王涛, 陈韬. 一种基于CNN-LSTM的改进CGAN光伏短期出力场景生成方法[J]. 太阳能学报. 2025, 46(4): 263-272 https://doi.org/10.19912/j.0254-0096.tynxb.2023-2045

Qin Weimin, Tang Hao, Ren Manman, Liang Xiao, Wang Tao, Chen Tao. IMPROVED CGAN PHOTOVOLTAIC SHORT-TERM OUTPUT SCENARIO GENERATION METHOD BASED ON CNN-LSTM[J]. Acta Energiae Solaris Sinica. 2025, 46(4): 263-272 https://doi.org/10.19912/j.0254-0096.tynxb.2023-2045

中图分类号： TM615 TP183

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