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arxiv: 2605.03165 · v1 · submitted 2026-05-04 · 📡 eess.SY · cs.SY

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High-Fidelity Full-Sky Video Prediction for Photovoltaic Ramp Event Forecasting

Siyuan Wang , Fengqi You
Authors on Pith no claims yet

Pith reviewed 2026-05-08 17:20 UTC · model grok-4.3

classification 📡 eess.SY cs.SY
keywords photovoltaic ramp forecastingfull-sky video predictioncloud motion modelingdiffusion modelstransformer architectureultra-short-term solar forecastgrid stabilityrenewable integration
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The pith

A generative framework predicts full-sky videos to forecast photovoltaic ramp events up to 16 minutes ahead with 10 percent higher critical success index.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that a combined model can generate realistic future sky videos from current images and then use those videos to predict sudden drops or rises in solar power output. It does this by modeling the motion of clouds at fine time scales, allowing forecasts at one-minute resolution. A sympathetic reader would care because rapid cloud changes cause ramp events that threaten grid stability in solar-heavy power systems. The work shows measurable gains in video quality metrics and in ramp detection accuracy over prior methods.

Core claim

The central claim is that a physics-informed diffusion network for full-sky video prediction, when coupled with a ramp-aware transformer for PV output, produces high-fidelity forecasts of cloud patterns and thereby enables more accurate ultra-short-term prediction of photovoltaic ramp events, delivering consistent gains in structural and temporal video fidelity plus a 10 percent rise in Critical Success Index for ramp detection.

What carries the argument

PhyDiffNet, a future sky video prediction model that uses diffusion to generate high-fidelity full-sky frames capturing chaotic cloud motion, paired with RaPVFormer, a ramp-aware PV output forecasting model that translates predicted cloud occlusions into irradiance and power forecasts.

If this is right

  • Forecasts become available 16 minutes in advance at one-minute resolution for grid operators.
  • Attention maps in the model highlight specific cloud regions that drive irradiance swings, aiding interpretability.
  • Video quality improves across structural similarity, perceptual, and temporal consistency measures.
  • The approach supports reduced reliance on reserve generation capacity during high solar penetration.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same video-prediction step could be adapted to forecast irradiance for other weather-sensitive assets such as wind or building loads.
  • Real-time fusion with additional sensors like lidar or satellite imagery might extend the reliable forecast horizon.
  • If the cloud-motion model generalizes across climates, it could serve as a drop-in component for regional grid simulators.

Load-bearing premise

That the generated sky videos faithfully reproduce the actual spatiotemporal cloud movements responsible for real irradiance variability.

What would settle it

A test on an independent dataset where the predicted videos show low correlation with measured irradiance changes or where the Critical Success Index gain falls below statistical significance.

read the original abstract

Accurate ultra-short-term forecasting of photovoltaic (PV) ramp events is essential for maintaining grid stability in solar-integrated power systems, particularly under rapidly changing cloud conditions. This paper presents a generative forecasting framework that integrates a future sky video prediction model (PhyDiffNet) with a ramp aware PV output forecasting model (RaPVFormer). Based on the relatively slow yet chaotic dynamics of cloud motion, the system forecasts ramp events up to 16 minutes in advance at a 1-minute resolution by capturing fine-grained spatiotemporal cloud patterns and generating high-fidelity full-sky video frames. Interpretability is enhanced through attention visualization, highlighting cloud occlusion regions that significantly influence irradiance variability. Supported by extensive quantitative evaluation, the proposed framework demonstrates state-of-the-art performance in both full-sky video prediction and PV output forecasting. It delivers consistent improvements in structural, perceptual, and temporal video quality, along with a 10% increase in Critical Success Index (CSI) for PV ramp detection. These results demonstrate the capability of AI driven multimodal sensing for ultra short term solar forecasting, supporting more reliable renewable integration and potentially reducing dependence on reserve capacity.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The manuscript proposes a generative forecasting framework that combines PhyDiffNet, a physics-informed diffusion model for high-fidelity full-sky video prediction of cloud dynamics, with RaPVFormer, a ramp-aware transformer for PV output forecasting. It claims to enable ultra-short-term (up to 16 min at 1-min resolution) prediction of PV ramp events by capturing spatiotemporal cloud patterns, with state-of-the-art performance in structural/perceptual/temporal video metrics and a 10% CSI improvement for ramp detection, supported by quantitative evaluation and attention-based interpretability.

Significance. If the empirical claims hold after proper validation, the multimodal integration of generative sky-video prediction with downstream PV ramp forecasting could meaningfully advance ultra-short-term solar forecasting for grid stability. The physics-informed approach to cloud motion and the use of attention visualization for interpretability are constructive elements that address a practical need in renewable integration.

major comments (2)
  1. [Abstract] Abstract: The assertion of state-of-the-art performance in full-sky video prediction and PV output forecasting, along with a specific 10% CSI gain for ramp detection, is presented without any baselines, datasets, error bars, ablation studies, or validation protocols. This absence makes it impossible to confirm that the data support the central quantitative claims.
  2. [Evaluation] Evaluation section (implied by 'extensive quantitative evaluation'): No ablation studies or causal analysis are described that isolate the contribution of PhyDiffNet-predicted videos to the reported CSI improvement in RaPVFormer. Without such controls (e.g., comparing predicted vs. ground-truth sky videos or error propagation from cloud-edge/optical-depth inaccuracies), it remains unclear whether the CSI lift arises from the video-prediction component or from the ramp-aware architecture alone.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address the major comments below and will make targeted revisions to improve the clarity and rigor of our quantitative claims and validation.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The assertion of state-of-the-art performance in full-sky video prediction and PV output forecasting, along with a specific 10% CSI gain for ramp detection, is presented without any baselines, datasets, error bars, ablation studies, or validation protocols. This absence makes it impossible to confirm that the data support the central quantitative claims.

    Authors: We acknowledge that the abstract, constrained by length, presents high-level claims without embedding the full evaluation details. The manuscript body (Evaluation section) reports comparisons against multiple baselines on the specified datasets, with quantitative metrics, error bars, and validation protocols. To mitigate this, we will revise the abstract to concisely reference the key baselines, dataset, and nature of the CSI improvement while pointing to the supporting experiments. revision: partial

  2. Referee: [Evaluation] Evaluation section (implied by 'extensive quantitative evaluation'): No ablation studies or causal analysis are described that isolate the contribution of PhyDiffNet-predicted videos to the reported CSI improvement in RaPVFormer. Without such controls (e.g., comparing predicted vs. ground-truth sky videos or error propagation from cloud-edge/optical-depth inaccuracies), it remains unclear whether the CSI lift arises from the video-prediction component or from the ramp-aware architecture alone.

    Authors: The current manuscript demonstrates overall framework superiority via end-to-end comparisons to non-video baselines but does not include dedicated ablations that directly isolate PhyDiffNet's predicted videos (e.g., RaPVFormer with ground-truth vs. predicted inputs or error propagation analysis). We agree this strengthens causal attribution. We will add these ablation studies and analyses in the revised manuscript. revision: yes

Circularity Check

0 steps flagged

No derivation chain or self-referential steps; performance claims rest on independent quantitative evaluation

full rationale

The paper presents an integrated generative framework (PhyDiffNet for full-sky video prediction + RaPVFormer for ramp-aware PV forecasting) and asserts SOTA results plus a 10% CSI gain based on 'extensive quantitative evaluation' of structural, perceptual, temporal, and ramp-detection metrics. No equations, first-principles derivations, fitted parameters renamed as predictions, or self-citations appear in the provided text that would reduce any claimed output to its inputs by construction. The central claims therefore remain empirically grounded rather than tautological, satisfying the default expectation of no significant circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review yields no identifiable free parameters, axioms, or invented entities. The framework is presumed to rest on standard deep-learning assumptions (e.g., differentiability of loss functions, stationarity of training distributions) that are not stated or audited here.

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Works this paper leans on

43 extracted references · 3 canonical work pages · 2 internal anchors

  1. [1]

    Snapshot of global PV markets 2025,

    G. Masson, A. Van Rechem, M. de l’Epine, A. Jäger -Waldau, and others, “Snapshot of global PV markets 2025,” International Energy Agency Photovoltaic Power Systems Programme (IEA PVPS), Technical Report Task 1, Apr. 2025. [Online]. Available: https://iea -pvps.org/wp- content/uploads/2025/04/Snapshot-of-Global-PV-Markets_2025.pdf

    2025

  2. [2]

    Improving ultra- short-term photovoltaic power forecasting using a novel sky -image-based framework considering spatial -temporal feature interaction,

    H. Zang, D. Chen, J. Liu, L. Cheng, G. Sun, and Z. Wei, “Improving ultra- short-term photovoltaic power forecasting using a novel sky -image-based framework considering spatial -temporal feature interaction,” Energy, vol. 293, p. 130538, Apr. 2024

    2024

  3. [3]

    NREL National Wind Technology Center (NWTC): M2 Tower; Boulder, Colorado (Data)

    D. Jager and A. Andreas, “NREL National Wind Technology Center (NWTC): M2 Tower; Boulder, Colorado (Data).” NREL Report No. DA - 5500-56489, 1996

    1996

  4. [4]

    All sky imaging -based short-term solar irradiance forecasting with Long Short-Term Memory networks,

    N. Y. Hendrikx et al., “All sky imaging -based short-term solar irradiance forecasting with Long Short-Term Memory networks,” Sol. Energy, vol. 272, p. 112463, Apr. 2024

    2024

  5. [5]

    Deep Learning Based Multistep Solar Forecasting for PV Ramp-Rate Control Using Sky Images,

    H. Wen et al., “Deep Learning Based Multistep Solar Forecasting for PV Ramp-Rate Control Using Sky Images,” IEEE Trans. Ind. Inform., vol. 17, no. 2, pp. 1397–1406, Feb. 2021

    2021

  6. [6]

    Mitigating the Impact of Photovoltaic Power Ramps on Intraday Economic Dispatch Using Reinforcement Forecasting,

    L. Cheng, H. Zang, A. Trivedi, D. Srinivasan, Z. Wei, and G. Sun, “Mitigating the Impact of Photovoltaic Power Ramps on Intraday Economic Dispatch Using Reinforcement Forecasting,” IEEE Trans. Sustain. Energy, vol. 15, no. 1, pp. 3–12, Jan. 2024

    2024

  7. [7]

    An Analysis of Storage Requirements and Benefits of Short -Term Forecasting for PV Ramp Rate Mitigation,

    D. Fregosi, N. Pilot, M. Bolen, and W. B. Hobbs, “An Analysis of Storage Requirements and Benefits of Short -Term Forecasting for PV Ramp Rate Mitigation,” IEEE J. Photovolt., vol. 13, no. 2, pp. 315–324, Mar. 2023

    2023

  8. [8]

    Reliant Monotonic Charging Controllers for Parallel -Connected Battery Storage Units to Reduce PV Power Ramp Rate and Battery Aging,

    A. A. Abdalla, M. S. E. Moursi, T. H. M. El -Fouly, and K. H. A. Hosani, “Reliant Monotonic Charging Controllers for Parallel -Connected Battery Storage Units to Reduce PV Power Ramp Rate and Battery Aging,” IEEE Trans. Smart Grid, vol. 14, no. 6, pp. 4424–4438, Nov. 2023

    2023

  9. [9]

    A PV ramp-rate control strategy to extend battery lifespan using forecasting,

    A. Gonzalez -Moreno, J. Marcos, I. de la Parra, and L. Marroyo, “A PV ramp-rate control strategy to extend battery lifespan using forecasting,” Appl. Energy, vol. 323, p. 119546, Oct. 2022

    2022

  10. [10]

    Advanced feature engineering in microgrid PV forecasting: A fast computing and data-driven hybrid modelling framework,

    A. Habib and J. Hossain, “Advanced feature engineering in microgrid PV forecasting: A fast computing and data-driven hybrid modelling framework,” Renew. Energy, vol. 224, pp. 1201–1217, 2024

    2024

  11. [11]

    Applications for solar irradiance nowcasting in the control of microgrids: A review,

    R. Samu et al., “Applications for solar irradiance nowcasting in the control of microgrids: A review,” Renew. Sustain. Energy Rev., vol. 147, p. 111187, Sept. 2021

    2021

  12. [12]

    Challenges of renewable energy penetration on power system stability: A review,

    S. Impram, I. H. Nese, and B. Oral, “Challenges of renewable energy penetration on power system stability: A review,” Energy Rep., vol. 6, pp. 109–125, 2020

    2020

  13. [13]

    Open-source sky image datasets for solar forecasting with deep learning: A comprehensive survey,

    Y. Nie, X. Li, Q. Paletta, M. Aragon, A. Scott, and A. Brandt, “Open-source sky image datasets for solar forecasting with deep learning: A comprehensive survey,” Renew. Sustain. Energy Rev., vol. 189, p. 113977, Jan. 2024

    2024

  14. [14]

    Intra -hour irradiance forecasting techniques for solar power integration: A review,

    Y. Chu, M. Li, C. F. M. Coimbra, D. Feng, and H. Wang, “Intra -hour irradiance forecasting techniques for solar power integration: A review,” iScience, vol. 24, no. 10, p. 103136, Oct. 2021

    2021

  15. [15]

    Day -ahead photovoltaic power forecasting based on corrected numeric weather prediction and domain generalization,

    M. Liu, Z. Lai, Y. Fang, and Q. Ling, “Day -ahead photovoltaic power forecasting based on corrected numeric weather prediction and domain generalization,” Energy Build., vol. 329, p. 115212, Feb. 2025

    2025

  16. [16]

    A review of distributed solar forecasting with remote sensing and deep learning,

    Y. Chu, Y. Wang, D. Yang, S. Chen, and M. Li, “A review of distributed solar forecasting with remote sensing and deep learning,” Renew. Sustain. Energy Rev., vol. 198, p. 114391, July 2024

    2024

  17. [17]

    Advances in solar forecasting: Computer vision with deep learning,

    Q. Paletta et al., “Advances in solar forecasting: Computer vision with deep learning,” Adv. Appl. Energy, vol. 11, p. 100150, Sept. 2023

    2023

  18. [18]

    Forecasting-Based Power Ramp-Rate Control Strategies for Utility-Scale PV Systems,

    X. Chen, Y. Du, H. Wen, L. Jiang, and W. Xiao, “Forecasting-Based Power Ramp-Rate Control Strategies for Utility-Scale PV Systems,” IEEE Trans. Ind. Electron., vol. 66, no. 3, pp. 1862–1871, Mar. 2019

    2019

  19. [19]

    Short -term irradiance forecasting using skycams: Motivation and development,

    S. R. West, D. Rowe, S. Sayeef, and A. Berry, “Short -term irradiance forecasting using skycams: Motivation and development,” Sol. Energy, vol. 110, pp. 188–207, Dec. 2014

    2014

  20. [20]

    A novel intra-hour PV output forecasting technique based on total-sky images,

    S. Zhang et al., “A novel intra-hour PV output forecasting technique based on total-sky images,” CSEE J. Power Energy Syst., pp. 1–11, 2024

    2024

  21. [21]

    Solar PV output prediction from video streams using convolutional neural networks,

    Y. Sun, G. Szűcs, and A. R. Brandt, “Solar PV output prediction from video streams using convolutional neural networks,” Energy Environ. Sci., vol. 11, no. 7, pp. 1811–1818, July 2018

    2018

  22. [22]

    Using sky-classification to improve the short-term prediction of irradiance with sky images and convolutional neural networks,

    V. A. Martinez Lopez, G. van Urk, P. J. F. Doodkorte, M. Zeman, O. Isabella, and H. Ziar, “Using sky-classification to improve the short-term prediction of irradiance with sky images and convolutional neural networks,” Sol. Energy, vol. 269, p. 112320, Feb. 2024

    2024

  23. [23]

    Convolutional LSTM network: a machine learning approach for precipitation nowcasting,

    X. Shi, Z. Chen, H. Wang, D. -Y. Yeung, W. Wong, and W. Woo, “Convolutional LSTM network: a machine learning approach for precipitation nowcasting,” in Advances in neural information processing systems, 2015, pp. 802 –810. [Online]. Available: https://papers.nips.cc/paper/5955-convolutional-lstm-network-a-machine- learning-approach-for-precipitation-nowca...

    2015

  24. [24]

    ECLIPSE: Envisioning CLoud Induced Perturbations in Solar Energy,

    Q. Paletta, A. Hu, G. Arbod, and J. Lasenby, “ECLIPSE: Envisioning CLoud Induced Perturbations in Solar Energy,” Appl. Energy, vol. 326, p. 119924, Nov. 2022

    2022

  25. [25]

    Disentangling physical dynamics from unknown factors for unsupervised video prediction,

    V. L. Guen and N. Thome, “Disentangling physical dynamics from unknown factors for unsupervised video prediction,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR), Seattle, WA, USA: IEEE, June 2020, pp. 11471–11481

    2020

  26. [26]

    A deep physical model for solar irradiance forecasting with fisheye images,

    V. L. Guen and N. Thome, “A deep physical model for solar irradiance forecasting with fisheye images,” in CVPR workshops, OmniCV workshop at the IEEE/CVF conference on computer vision and pattern recognition (CVPRW), June 2020, pp. 2685–2688

    2020

  27. [27]

    VideoGPT: Video Generation using VQ-VAE and Transformers

    W. Yan, Y. Zhang, P. Abbeel, and A. Srinivas, “VideoGPT: Video Generation using VQ -VAE and Transformers,” Sept. 14, 2021, arXiv: arXiv:2104.10157

    work page internal anchor Pith review arXiv 2021

  28. [28]

    SkyGPT: Probabilistic ultra-short-term solar forecasting using synthetic sky images from physics -constrained VideoGPT,

    Y. Nie, E. Zelikman, A. Scott, Q. Paletta, and A. Brandt, “SkyGPT: Probabilistic ultra-short-term solar forecasting using synthetic sky images from physics -constrained VideoGPT,” Adv. Appl. Energy , vol. 14, p. 100172, July 2024

    2024

  29. [29]

    Sky image-based solar forecasting using deep learning with heterogeneous multi-location data: Dataset fusion versus transfer learning,

    Y. Nie et al., “Sky image-based solar forecasting using deep learning with heterogeneous multi-location data: Dataset fusion versus transfer learning,” Appl. Energy, vol. 369, p. 123467, Sept. 2024

    2024

  30. [30]

    PV -ramp

    S. Wang, “PV -ramp.” 2023. [Online]. Available: https://github.com/PEESEgroup/PV-Ramp

    2023

  31. [31]

    PV power output prediction from sky images using convolutional neural network: The comparison of sky-condition-specific sub-models and an end-to-end model,

    Y. Nie, Y. Sun, Y. Chen, R. Orsini, and A. Brandt, “PV power output prediction from sky images using convolutional neural network: The comparison of sky-condition-specific sub-models and an end-to-end model,” J. Renew. Sustain. Energy, vol. 12, no. 4, p. 046101, July 2020

    2020

  32. [32]

    Image quality assessment: From error visibility to structural similarity,

    Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: From error visibility to structural similarity,” IEEE Trans. Image Process., vol. 13, no. 4, pp. 600–612, 2004

    2004

  33. [33]

    Supplementary File for High-Fidelity Full- Sky Video Prediction for Photovoltaic Ramp Event Forecasting

    Siyuan Wang and Fengqi You, “Supplementary File for High-Fidelity Full- Sky Video Prediction for Photovoltaic Ramp Event Forecasting.” [Online]. Available: https://doi.org/10.6084/m9.figshare.30899570

    work page doi:10.6084/m9.figshare.30899570

  34. [34]

    Denoising diffusion probabilistic models,

    J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” in Advances in neural information processing systems 33 (NeurIPS 2020) , Curran Associates, Inc., 2020, pp. 6840–6851

    2020

  35. [35]

    Deep Residual Learning for Image Recognition

    K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” Dec. 10, 2015, arXiv: arXiv:1512.03385

    work page internal anchor Pith review arXiv 2015

  36. [36]

    SKIPP’D: A SKy Images and Photovoltaic Power Generation Dataset for short -term solar forecasting,

    Y. Nie, X. Li, A. Scott, Y. Sun, V. Venugopal, and A. Brandt, “SKIPP’D: A SKy Images and Photovoltaic Power Generation Dataset for short -term solar forecasting,” Sol. Energy, vol. 255, pp. 171–179, May 2023

    2023

  37. [37]

    2017 Sky Images and Photovoltaic Power Generation Dataset for Short-term Solar Forecasting (Stanford Raw),

    Y. Nie, X. Li, A. Scott, and A. Brandt, “2017 Sky Images and Photovoltaic Power Generation Dataset for Short-term Solar Forecasting (Stanford Raw),” Stanford Research Data. Aug. 01, 2022

    2017

  38. [38]

    Empire AI: A new model for provisioning AI and HPC for academic research in the public good,

    S. Bloom et al., “Empire AI: A new model for provisioning AI and HPC for academic research in the public good,” in Practice and experience in advanced research computing (PEARC ’25), New York, NY, USA: ACM, July 2025, p. 4

    2025

  39. [39]

    R. C. Gonzalez and R. E. Woods, Digital image processing, 2nd ed. Prentice Hall, 2002

    2002

  40. [40]

    The unreasonable effectiveness of deep features as a perceptual metric,

    R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang, “The unreasonable effectiveness of deep features as a perceptual metric,” in Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), 2018, pp. 586–595

    2018

  41. [41]

    Very deep convolutional networks for large‑scale image recognition,

    K. Simonyan and A. Zisserman, “Very deep convolutional networks for large‑scale image recognition,” in Proceedings of the international conference on learning representations (ICLR), 2015

    2015

  42. [42]

    RAFT: Recurrent all-pairs field transforms for optical flow,

    Z. Teed and J. Deng, “RAFT: Recurrent all-pairs field transforms for optical flow,” in European conference on computer vision (ECCV), Springer, 2020, pp. 402–419

    2020

  43. [43]

    Grad‑CAM: Visual explanations from deep networks via gradient‑based localization,

    R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad‑CAM: Visual explanations from deep networks via gradient‑based localization,” in Proceedings of the IEEE international conference on computer vision (ICCV), IEEE, 2017, pp. 618–626

    2017