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

Qin Weimin; Tang Hao; Ren Manman; Liang Xiao; Wang Tao; Chen Tao

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

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (4) : 263-272. DOI: 10.19912/j.0254-0096.tynxb.2023-2045

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

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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.

Key words

photovoltaic power / scenario generation / generative adversarial networks / long short term memory networks / convolutional neural network / uncertainty

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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

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