التفاصيل البيبلوغرافية
| العنوان: |
SkyGPT: Probabilistic ultra-short-term solar forecasting using synthetic sky images from physics-constrained VideoGPT |
| المؤلفون: |
Yuhao Nie, Eric Zelikman, Andea Scott, Quentin Paletta, Adam Brandt |
| المصدر: |
Advances in Applied Energy, Vol 14, Iss , Pp 100172- (2024) |
| بيانات النشر: |
Elsevier, 2024. |
| سنة النشر: |
2024 |
| مصطلحات موضوعية: |
Cloud motion prediction, Probabilistic solar forecasting, Deep learning, Generative models, Stochastic video prediction, Sky images, Energy industries. Energy policy. Fuel trade, HD9502-9502.5 |
| الوصف: |
The variability of solar photovoltaic (PV) power output, driven by rapidly changing cloud dynamics, hinders the transition to reliable renewable energy systems. Information on future sky conditions, especially cloud coverage, holds the promise for improving PV output forecasting. Leveraging recent advances in generative artificial intelligence (AI), we introduce SkyGPT, a physics-constrained stochastic video prediction model, which predicts plausible future images of the sky using historical sky images. We show that SkyGPT can accurately capture cloud dynamics, producing highly realistic and diverse future sky images. We further demonstrate its efficacy in 15-minute-ahead probabilistic PV output forecasting using real-world power generation data from a 30-kW rooftop PV system. By coupling SkyGPT with a U-Net-based PV power prediction model, we observe superior prediction reliability and sharpness compared with several benchmark methods. The propose approach achieves a continuous ranked probability score (CRPS) of 2.81 kW, outperforming a classic convolutional neural network (CNN) baseline by 13% and the smart persistence model by 23%. The findings of this research could aid efficient and resilient management of solar electricity generation, particularly as we transition to renewable-heavy grids. The study also provides valuable insights into stochastic cloud modeling for a broad research community, encompassing fields such as solar energy meteorology and atmospheric sciences. |
| نوع الوثيقة: |
article |
| وصف الملف: |
electronic resource |
| اللغة: |
English |
| تدمد: |
2666-7924 |
| Relation: |
http://www.sciencedirect.com/science/article/pii/S2666792424000106; https://doaj.org/toc/2666-7924 |
| DOI: |
10.1016/j.adapen.2024.100172 |
| URL الوصول: |
https://doaj.org/article/3224e1d0874049d5ac45899e009f8271 |
| رقم الانضمام: |
edsdoj.3224e1d0874049d5ac45899e009f8271 |
| قاعدة البيانات: |
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