State Transport Routing for Short-horizon Adaptation in Multi-horizon Photovoltaic Forecasting

Abstract:Recent power measurements provide valuable information for photovoltaic(PV) power forecasting, but directly extrapolating short-term trends can introduce substantial errors over longer forecast horizons. To address this challenge, we propose state transport routing (STR), a lightweight adapter that refines the predictions of a frozen forecasting model. STR combines the original forecast with two complementary trajectories derived from the latest measured power level and its recent trend. A horizon-conditioned router adjusts their contributions over the first 120 min, while leaving subsequent predictions unchanged. Experiments on four public PV datasets show that STR consistently outperforms a parameter-matched residual adapter. On PVDAQ, the same approach improves five neural forecasting backbones, reducing all-horizon normalized mean absolute error by 0.0201-0.2364 percentage points, with paired 95% confidence intervals excluding zero. No reliable improvement is observed for LightGBM. These findings demonstrate the potential of structured state adaptation to improve short-term forecasting across different neural architectures without retraining the underlying models or altering their longer-horizon predictions.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2609.36926 [cs.LG]

(or arXiv:2609.36926v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.36926 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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