状态传输路由用于多时域光伏预测中的短期适应

中文摘要 AI 辅助

近期功率测量为光伏（PV）功率预测提供了有价值的信息，但直接外推短期趋势在较长的预测时域上可能引入显著误差。为应对这一挑战，我们提出了状态传输路由（STR），一种轻量级适配器，用于优化冻结预测模型的预测结果。STR将原始预测与源自最新实测功率水平及其近期趋势的两条互补轨迹相结合。一个依赖于时域的路由器在前120分钟内调整它们的贡献，同时保持后续预测不变。在四个公开光伏数据集上的实验表明，STR始终优于参数匹配的残差适配器。在PVDAQ上，相同方法改进了五个神经预测骨干网络，将所有时域归一化平均绝对误差降低了0.0201至0.2364个百分点，配对95%置信区间不包含零。对于LightGBM未观察到可靠改进。这些发现证明了结构化状态适应在无需重新训练底层模型或改变其更长时域预测的情况下，提升不同神经架构短期预测的潜力。

英文摘要

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.

发表机构

• Huaibei Normal University（淮北师范大学）

机构由 AI 辅助整理，请以论文原文为准。