GateFormer is a vision‑transformer model that forecasts intra‑hour global horizontal irradiance (GHI) from sky images by decomposing the forecast into a trajectory prediction and a delta prediction, then adaptively weighting these two outputs. It incorporates recent GHI measurements into the visual token stream, enabling self‑attention between image patches and irradiance data. On multiple benchmarks (NREL‑TSI, Chilbolton, SIRTA) GateFormer achieves the lowest final‑horizon root‑mean‑square error, outperforming state‑of‑the‑art transformers and demonstrating significant gains during rapid irradiance ramps.
By David Hamlyn, Sunny Chaudhary, Jonathon Hare, Tasmiat Rahman
The paper introduces SolCloudLLM, a large language model–based framework that fuses sky‑image patches with time‑series data through bidirectional multimodal fusion for short‑term solar forecasting. Experiments on the SIRTA and SKIPP'D datasets show that SolCloudLLM outperforms existing baselines, achieving up to a 25.4% reduction in mean squared error, especially under cloudy conditions and in few‑shot scenarios.
By Ken Chen, Maneesha Perera, Wei Wang, Sachith Seneviratne, Hansani Weeratunge, Saman Halgamuge
arXiv:2610.40418v1 Announce Type: cross
Abstract: This work develops a physics-informed multimodal deep learning framework for 10- to 110-minute-ahead probabilistic photovoltaic power forecasting. Th...
By Anushka Katiyar, Cong Feng, Jing Wang, Tristan Liu, Simon Julien, Zachary Jacobs
The paper introduces a dual‑framework Vision Transformer (ViT) method for estimating diffuse horizontal irradiance (DHI) from sky images in data‑scarce settings. It combines physics‑based atmospheric parameters, a sequential pipeline that first predicts synthetic global horizontal irradiance (GHI), and a dual‑parallel architecture that processes raw and physics‑informed overlay‑enhanced fisheye images. Tested on Chilbolton Observatory data, the approach achieves RMSE within 2–6 W/m² of a ViT trained on a full dataset, demonstrating a scalable, cost‑effective solution for solar resource assessment when long‑term sky imagery is limited.
By David Hamlyn, Sunny Chaudhary, Tasmiat Rahman
arXiv:2606. 19026v1 Announce Type: cross Abstract: Forecast errors in high-resolution numerical weather prediction (NWP) systems are often linked to unresolved planetary boundary layer (PBL) processes, convection, terrain-induced circulations, and other vertically structured atmospheric phenomena.
By David Aaron Evans, Jay C. Rothenberger, Kara J. Sulia, Nick P. Bassill, Chris D. Thorncroft
The paper presents a hierarchical ensemble for short‑term photovoltaic power forecasting that fuses temporal neural models, historical analogs, state climatology, and gradient‑boosted trees with horizon‑specific convex weights. Solar geometry and numerical weather forecasts provide expected generation conditions, while a calibration step corrects recent bias using historical forecast errors. Evaluated on PVDAQ and GEFCom2014 data, the ensemble reduces mean absolute error by up to 6% compared to strong individual models, though its advantage varies with dataset and horizon.
By Xu Yuqing, Zhou Liguo, Sun Ze, Yu Lei, Jiang Mingming