arXiv AI
IMPACT: Inverter-Informed Multimodal Learning for Photovoltaic Generation Forecasting
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GateFormer: Forecast-Space Decomposition for Intra-Hour Solar Irradiance Prediction from Sky Images
GateFormer is a dual‑decoder Vision Transformer that forecasts intra‑hour global horizontal irradiance (GHI) from sky images by embedding observed GHI into the visual token stream and predicting both a future GHI trajectory and the final‑horizon change. The two predictions are adaptively weighted in prediction space, with magnitude‑ and direction‑supervision to reduce remaining error. On multiple benchmarks, GateFormer achieves the lowest final‑horizon RMSE, outperforming state‑of‑the‑art transformers and showing significant gains especially during positive ramp events.