Vision Transformers for Estimating Irradiance Using Data Scarce Sky Images

Accurate estimation of diffuse horizontal irradiance (DHI) is critical for optimising photovoltaic system performance and energy forecasting yet remains challenging in regions lacking comprehensive ground-based instrumentation. Recent advancements using Vision Transformers (ViTs) trained on extensive sky image datasets have shown promise in replacing costly irradiance measurement equipment, but the scarcity of long-term, high-quality sky imagery significantly restricts practical implementation. Addressing this critical gap, this study proposes a novel dual-framework approach designed for data-scarce scenarios. First, calculated atmospheric parameters, including extraterrestrial irradiance and cyclic time encodings, are integrated to represent sky conditions without utilising any instrumentation. Next, a sequential pipeline initially predicts synthetic global horizontal irradiance (GHI) and uses it as a feature, to refine DHI estimation. Finally, a dual-parallel architecture simultaneously processes raw and overlay-enhanced fisheye sky images. Overlays are generated through unsupervised, physics-informed cloud segmentation to highlight dynamic sky features. Empirical validation is performed using data from the Chilbolton Observatory, chosen for its temperate climate and frequent cloud variability. To simulate data-scarce conditions, models are trained on a single month (e.g., January) and evaluated across a temporally disjoint, full-year test set. Under this setup, the sequential and dual-parallel frameworks achieve RMSE values within 2-3 W/m^2 and 1-6 W/m^2, respectively, of a state-of-the-art ViT trained on the complete dataset. By combining physics-informed modelling with unsupervised segmentation, the proposed method provides a scalable and cost-effective solution for DHI estimation, advancing solar resource assessment in data-constrained environments.

David Hamlyn, Sunny Chaudhary, Tasmiat Rahman

Leverage2 / 5

Reusable within one subfield (a technique, dataset, or protocol a few groups will adopt).

16% of this desk’s rubric

Magnitude3 / 5

Large gain: roughly 2x, or a clear new state of the art on a hard, unsaturated problem.

20% of this desk’s rubric

Evidence3 / 5

Solid: multiple benchmarks or cohorts, ablations, fair baselines, released code or data.

20% of this desk’s rubric

Novelty2 / 5

A new combination of known ideas.

10% of this desk’s rubric

Trajectory2 / 5

Some room to improve with obvious engineering.

14% of this desk’s rubric

Stakes3 / 5

Meaningful benefit to many people within a few years.

20% of this desk’s rubric

No attention signals recorded yet.

• Reviewed by heuristic-v7 on Oct 23, 2026, 07:03 UTC. Paper type: empirical.
• Categories: eess.IV, cs.AI, cs.CV, cs.LG
• TOP, No.4 in the Climate & Energy edition of October 23, 2026.