LONG-TERM PHOTOVOLTAIC POWER OUTPUT SCENARIO GENERATION METHOD BASED ON IMPROVED TRANSFORMER-CGAN

Ye Yujiang; Xuan Shunde; Shi Ruifeng; Jia Limin

doi:10.19912/j.0254-0096.tynxb.2025-0529

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (8) : 169-177. DOI: 10.19912/j.0254-0096.tynxb.2025-0529

LONG-TERM PHOTOVOLTAIC POWER OUTPUT SCENARIO GENERATION METHOD BASED ON IMPROVED TRANSFORMER-CGAN

Ye Yujiang¹, Xuan Shunde¹, Shi Ruifeng^1,2, Jia Limin^2,3

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Abstract

To overcome the limitations of traditional methods in effectively generating long-term photovoltaic （PV） power output uncertainty scenarios, this paper proposes a novel approach that integrates an improved Transformer model with a conditional generative adversarial network （CGAN）. Specifically, a Transformer-CGAN （T-CGAN） architecture is developed for generating long-term PV output scenarios. To better capture the inherent periodicity of PV output, a multi-timescale periodic attention mechanism is introduced, enabling the model to learn both short-term and long-term temporal patterns. A case study using historical data from a PV power plant in Northwest China is conducted to compare the proposed method with three benchmark models. The performance is evaluated from three perspectives： statistical accuracy, temporal correlation, and scenario validity. Experimental results demonstrate that the proposed approach significantly improves the accuracy of scenario generation, effectively captures temporal dependencies across multiple time scales, and enables efficient generation of annual PV output scenario sets.

Key words

photovoltaic power generation / generative adversarial networks / Transformer / scenario generation / periodic attention mechanism

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Ye Yujiang, Xuan Shunde, Shi Ruifeng, Jia Limin. LONG-TERM PHOTOVOLTAIC POWER OUTPUT SCENARIO GENERATION METHOD BASED ON IMPROVED TRANSFORMER-CGAN[J]. Acta Energiae Solaris Sinica. 2026, 47(8): 169-177 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0529

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