光伏功率预测的视界特定专家融合

中文摘要 AI 辅助

短期光伏功率预测要求模型能够表征规律性太阳周期和天气驱动的波动，而这些因素的重要性随预测视界而变化。本研究开发了一种分层集成方法，结合了时间神经模型、历史相似日、状态气候学和梯度提升树。太阳几何和数值天气预报描述了预期的发电条件，而视界特定的凸权重用于组合互补的预测。一个独立的校准步骤利用可用的历史预测误差来考虑近期偏差。该框架在公开的PVDAQ数据上以15至240分钟的视界进行评估，并在GEFCom2014的3个太阳区域上以长达4小时的小时视界进行评估。在PVDAQ上，集成模型实现了日光容量归一化平均绝对误差为4.315%，相对于全特征LightGBM误差降低了4.11%，相对于微调的Chronos-2在相同校准下误差降低了6.03%。专家移除实验识别了集成内部的冗余。在GEFCom2014上使用3个训练种子，学习到的融合优于等权重，但与LightGBM表现相当。结果支持视界特定组合作为一种有用的预测策略，同时表明其相对于强个体模型的优势取决于数据集和评估周期。

英文摘要

Short-term photovoltaic power forecasting requires models to represent regular solar cycles and weather-driven fluctuations whose importance changes with the forecast horizon. This study develops a hierarchical ensemble that combines temporal neural models, historical analogs, state climatology, and gradient-boosted trees. Solar geometry and numerical weather forecasts describe the expected generation conditions, while horizon-specific convex weights combine complementary predictions. A separate calibration step uses available historical forecast errors to account for recent bias. The framework is evaluated on public PVDAQ data at 15--240-minute horizons and on three GEFCom2014 solar zones at hourly horizons up to four hours. On PVDAQ, the ensemble achieves a daylight capacity-normalized mean absolute error of 4.315%, reducing error by 4.11% relative to full-feature LightGBM and by 6.03% relative to fine-tuned Chronos-2 under identical calibration. Expert-removal experiments identify redundancy within the ensemble. Across three training seeds on GEFCom2014, learned fusion improves upon equal weighting but performs comparably to LightGBM. The results support horizon-specific combination as a useful forecasting strategy while showing that its advantage over strong individual models depends on the dataset and evaluation period.

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。