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A solar radiation data generation method for solar energy utilization scenarios: BIPV generation forecasting as a case study

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  • Jin, Yujia
  • Xing, Haowei
  • Zhu, Han
  • Li, Zhengrong
  • Wu, Chao

Abstract

Accurate photovoltaic (PV) forecasting is crucial for grid stability, yet data-driven models are often hindered by the “cold-start” problem of data scarcity at new installations. Current generative models that directly synthesize power data act as “black-box” solutions, lacking physical interpretability and generalizability. To address this, we propose StochRad-UAGAN, a novel gray-box GAN framework. Instead of generating power data, it synthesizes physically-consistent solar radiation scenarios by modeling the key stochastic driver: the cloud-induced daily attenuation coefficient (μDNI(t)). Our U-Net Attention GAN (UAGAN) proves superior to baseline models in capturing complex time-series features. In a BIPV forecasting case study, using our framework to augment a limited dataset reduced the prediction RMSE of various models by 10.1 %–49.7 %, with an optimal generated-to-real data ratio of 2:1 to 4:1. This validates StochRad-UAGAN as an effective, generalizable solution to the data scarcity problem in solar applications, bridging deep learning with physical principles.

Suggested Citation

  • Jin, Yujia & Xing, Haowei & Zhu, Han & Li, Zhengrong & Wu, Chao, 2026. "A solar radiation data generation method for solar energy utilization scenarios: BIPV generation forecasting as a case study," Renewable Energy, Elsevier, vol. 259(C).
    • Handle: RePEc:eee:renene:v:259:y:2026:i:c:s096014812502436x
    DOI: 10.1016/j.renene.2025.124772
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    References listed on IDEAS

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    1. Qi, Yuchen & Hu, Wei & Dong, Yu & Fan, Yue & Dong, Ling & Xiao, Ming, 2020. "Optimal configuration of concentrating solar power in multienergy power systems with an improved variational autoencoder," Applied Energy, Elsevier, vol. 274(C).
    2. Muhammad Naveed Akhter & Saad Mekhilef & Hazlie Mokhlis & Ziyad M. Almohaimeed & Munir Azam Muhammad & Anis Salwa Mohd Khairuddin & Rizwan Akram & Muhammad Majid Hussain, 2022. "An Hour-Ahead PV Power Forecasting Method Based on an RNN-LSTM Model for Three Different PV Plants," Energies, MDPI, vol. 15(6), pages 1-21, March.
    3. Chen, Zhiqiang & Li, Jianbin & Cheng, Long & Liu, Xiufeng, 2023. "Federated-WDCGAN: A federated smart meter data sharing framework for privacy preservation," Applied Energy, Elsevier, vol. 334(C).
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