arXiv cs.AI· Dayin Chen, Xiaodan Shi, Xin Xu, Zhongyue Su, Shibo Zhu, Dongxiao Zhang, Yuntian Chen, Jinyue Yan·· 3 天前AI 评分22
SatPV-Field：构建区域光伏发电场提升超短期分布式太阳能预测
Constructed regional photovoltaic generation fields improve ultra-short-term distributed solar forecasting
AI 导读
SatPV-Field 将分布式光伏测量数据转化为图像式区域发电场并建模其时空演化，结合卫星短波辐射与气象预报，为单站预测提供位置化指导。在 496 个分布式光伏系统（446 个源站、50 个留出目标站）实验中，该方法将站点级 MAE 较最佳单站时序基线降低约 10.0%，较基于图的区域预测方法降低 2.5%。
正文 · 原文
Abstract:Ultra-short-term forecasting of densely distributed photovoltaic (PV) systems remains challenging because external meteorological and satellite products may not adequately resolve rapid local variations, while heterogeneous PV systems exhibit different generation responses under similar atmospheric conditions. Meanwhile, dense PV fleets provide spatially distributed measurements that reveal regional generation dynamics. Here, we reformulate fleet-level PV forecasting from modeling discrete station time series to predicting explicit, spatially structured regional PV generation fields. We develop SatPV-Field, a regional-to-local forecasting framework that transforms distributed PV measurements into image-like generation fields and models their spatiotemporal evolution. By combining historical PV fields with satellite-derived future shortwave radiation guidance and meteorological forecasts, the framework predicts future regional generation states, which subsequently provide location-specific guidance for individual-station forecasting. Experiments involving 496 distributed PV systems, including 446 source stations and 50 held-out target stations, demonstrate that these regional fields can be effectively forecast and used to improve station-level predictions. The proposed approach reduces station-level MAE by approximately 10.0% relative to the best-performing single-station temporal baseline and by 2.5% relative to a graph-based regional forecasting approach using the same source fleet. These findings establish an explicit spatial-field modeling paradigm that connects regional generation dynamics with individual-site forecasting.
| Subjects: | Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE) |
| Cite as: | arXiv:2610.16110 [cs.AI] |
| (or arXiv:2610.16110v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.16110 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Dayin Chen [view email]
[v1]
Mon, 12 Oct 2026 03:17:25 UTC (1,425 KB)
来源：arXiv cs.AI · arxiv.org