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Research paper · Energy and AI · 2026

Forecast-space decomposition for intra-hour solar irradiance prediction from sky images

David Hamlyn · Sunny Chaudhary · Jonathon Hare · Tasmiat Rahman

University of Southampton

Complementary forecasts, one shared model.

GateFormer forecasts global horizontal irradiance (GHI) from sky imagery and recent irradiance measurements. A shared, GHI-conditioned Vision Transformer feeds two complementary predictors: one forecasts the future trajectory, while the other predicts change at the final horizon. A learned gate blends their forecasts, followed by a residual correction.

Sky images and measured GHI feed a shared ViT, followed by trajectory and delta heads, adaptive blending, and residual correction to produce the final forecast.
Conceptual overview. See the paper for the full architecture and conditioning inputs.

Results at a glance

125.01 W/m²RMSE · NREL-TSI · +30 minutes
9.6% lowerRMSE versus SIAT
Paired-timestamp evaluation
22.97–28.86%Forecast skill over smart persistence
Across three sites

Evaluated on NREL-TSI, Chilbolton, and SIRTA. Figures are from the published paper; comparisons follow its evaluation protocol.

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Cite this work

If this work is useful to your research, please cite the paper.

@article{hamlyn2026gateformer,
  title   = {{GateFormer}: Forecast-space decomposition for intra-hour solar irradiance prediction from sky images},
  author  = {Hamlyn, David and Chaudhary, Sunny and Hare, Jonathon and Rahman, Tasmiat},
  journal = {Energy and AI},
  volume  = {26},
  pages   = {100917},
  year    = {2026},
  doi     = {10.1016/j.egyai.2026.100917},
  url     = {https://doi.org/10.1016/j.egyai.2026.100917}
}

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