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Hugging Face Trending Papers

Reducing Multi-Horizon Photovoltaic Forecast Error through Tree-Anchored Expert Combination

The paper investigates whether combining a strong tree-based photovoltaic forecast model with other temporal predictors can lower its mean absolute error (MAE) over 15–240 minute horizons. A weighted ensemble, constrained to nonnegative weights that sum to one, is trained to minimize MAE, allowing unhelpful models to receive zero weight while the tree anchors the combination. On 56,078 windows from two co-located PVDAQ systems, the shared-MAE policy reduces scaled MAE by 3.21% relative to the tree alone, but adding the latest bounded neural correction does not improve performance further.

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arXiv AI
Sep 15

Horizon-specific Expert Fusion for Photovoltaic Power Forecasting

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
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arXiv AI
Sep 15

Ensemble Complexity in Photovoltaic Forecasting

arXiv:2609.15049v1 Announce Type: cross Abstract: An ensemble can improve photovoltaic forecasts while adding components that contribute little or increase computation. We assess these effects throug...

By Sun Ze, Zhou Liguo, Xu Yuqing, Yu Lei, Jiang Mingming
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Hugging Face Trending Papers
Oct 22

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.

llmsmultimodalbenchmarks
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arXiv Machine Learning
Oct 23

GateFormer: Forecast-Space Decomposition for Intra-Hour Solar Irradiance Prediction from Sky Images

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
llmsmultimodalbenchmarks
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Hugging Face Trending Papers
Jul 14

Robustness of Deep Learning Models for PV Power Forecasting under NWP Forecast Errors: A Spatiotemporal and Physically Interpretable Analysis

Engineering use of AI forecasting models requires not only high nominal accuracy but also predictable behavior under uncertain inputs. In photovoltaic (PV) forecasting, this requirement is especially challenging because numerical weather prediction (NWP) errors are temporally correlated, state dependent, and physically coupled across variables.

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arXiv Machine Learning
Sep 23

PROSWIN: Probabilistic Solar Wind Speed Forecasting Using Deep Distributional Regression From Solar Images

PROSWIN is a probabilistic machine learning model that forecasts hourly solar wind speed at Earth up to four days ahead, using solar images and magnetograms processed by a deep neural network and distributional regression. It introduces a prediction score metric that rewards both timeline and high‑speed solar wind peak accuracy, achieving well‑calibrated uncertainties and superior performance on 14 years of data compared to existing models. The study highlights the importance of the 171 Å channel and demonstrates that probabilistic forecasts outperform single‑value models for both overall timelines and peak events.

By Daniel Collin, Yuri Shprits, Luca Chiarabini, Stefan J. Hofmeister, Nadja Klein, Guillermo Gallego
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