SYNERGISTIC PREDICTION METHOD FOR SKY IMAGES AND PHOTOVOLTAIC POWER BASED ON TRANSFORMER-MAMBA HYBRID ARCHITECTURE

Shi Chongyang; Cao Tengfei; Li Kunchang; Chen Laijun

doi:10.19912/j.0254-0096.tynxb.2025-0689

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (9) : 219-228. DOI: 10.19912/j.0254-0096.tynxb.2025-0689

SYNERGISTIC PREDICTION METHOD FOR SKY IMAGES AND PHOTOVOLTAIC POWER BASED ON TRANSFORMER-MAMBA HYBRID ARCHITECTURE

Shi Chongyang^1,2, Cao Tengfei^1,2, Li Kunchang³, Chen Laijun⁴

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Abstract

Accurately forecasting sky imagery and its impact on solar power generation is essential for intelligent grid scheduling and the efficient management of renewable energy resources. However, capturing the dynamic evolution of cloud patterns and their complex, nonlinear relationship with photovoltaic （PV） power output remains a formidable challenge. To tackle this problem, we propose a multimodal deep learning framework that integrates Transformer and Mamba architectures, effectively leveraging both spatiotemporal features from sky images and infrared spectral information to improve forecasting precision. Within this framework, the Transformer is responsible for modeling the temporal dynamics of cloud motion, while Mamba focuses on extracting salient features from infrared spectra. A cross-modal feature fusion strategy is employed to learn the intricate nonlinear mapping between sky imagery and power output. We evaluate our model on the Stanford sky image and PV generation datasets, conducting extensive experiments that demonstrate superior performance in both sky image forecasting and PV power prediction tasks. Ablation studies reveal that the full model—incorporating infrared data, temporal modeling, and multi-scale feature extraction—achieves an RMSE of 13.04kW and an SSIM of 0.94653 for image prediction. Moreover,the proposed method achieves an MAE of 1.50 kW, approximately 4.5% lower than that of the second-best Tau model.Under typical cloudy weather conditions, the model is able to effectively track the trend of photovoltaic power fluctuations caused by cloud occlusion.

Key words

PV power generation / photointerpretation / multimodal feature fusions / state space methods

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Shi Chongyang, Cao Tengfei, Li Kunchang, Chen Laijun. SYNERGISTIC PREDICTION METHOD FOR SKY IMAGES AND PHOTOVOLTAIC POWER BASED ON TRANSFORMER-MAMBA HYBRID ARCHITECTURE[J]. Acta Energiae Solaris Sinica. 2026, 47(9): 219-228 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0689

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