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.
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Cite this article
APA 7
Yuqing, X., Liguo, Z., Ze, S., Lei, Y., & Mingming, J. (2026). State Transport Routing for Short-horizon Adaptation in Multi-horizon Photovoltaic Forecasting. https://omanscience.com/en/articles/state-transport-routing-for-short-horizon-adaptation-in-multi-horizon-photovoltaic-forecasting
MLA 9
Yuqing, Xu, et al. "State Transport Routing for Short-horizon Adaptation in Multi-horizon Photovoltaic Forecasting." https://omanscience.com/en/articles/state-transport-routing-for-short-horizon-adaptation-in-multi-horizon-photovoltaic-forecasting.
Chicago (author–date)
Yuqing, Xu, Zhou Liguo, Sun Ze, Yu Lei, and Jiang Mingming. 2026. "State Transport Routing for Short-horizon Adaptation in Multi-horizon Photovoltaic Forecasting." https://omanscience.com/en/articles/state-transport-routing-for-short-horizon-adaptation-in-multi-horizon-photovoltaic-forecasting.
Harvard
Yuqing, X., Liguo, Z., Ze, S., Lei, Y. and Mingming, J. (2026) 'State Transport Routing for Short-horizon Adaptation in Multi-horizon Photovoltaic Forecasting', Available at: https://omanscience.com/en/articles/state-transport-routing-for-short-horizon-adaptation-in-multi-horizon-photovoltaic-forecasting.
Vancouver
Yuqing X, Liguo Z, Ze S, Lei Y, Mingming J. State Transport Routing for Short-horizon Adaptation in Multi-horizon Photovoltaic Forecasting. https://omanscience.com/en/articles/state-transport-routing-for-short-horizon-adaptation-in-multi-horizon-photovoltaic-forecasting
IEEE
X. Yuqing, Z. Liguo, S. Ze, Y. Lei, and J. Mingming, "State Transport Routing for Short-horizon Adaptation in Multi-horizon Photovoltaic Forecasting," https://omanscience.com/en/articles/state-transport-routing-for-short-horizon-adaptation-in-multi-horizon-photovoltaic-forecasting.