融合卫星与地面数据，提升太阳能辐照度预测精度与分辨率
SolarCrossFormer: Improving day-ahead Solar Irradiance Forecasting by Integrating Satellite Imagery and Ground Sensors
- 用图神经网络融合卫星图像与地面气象数据
- 15分钟分辨率，24小时预报，瑞士127个地点误差仅6.1%
- 无需重训即可新增数据，无数据点也能预测
准确的日前太阳能辐照度预测对于大规模光伏系统并网至关重要。然而，现有方案在时间与空间分辨率上仍不满足系统运营商需求。本文提出SolarCrossFormer，一种新型深度学习模型，结合卫星影像与地面气象站时间序列数据。该模型利用创新的图神经网络捕捉输入数据的跨模态与内模态相关性，提升预测精度与分辨率。可生成瑞士任意位置、15分钟分辨率、长达24小时的概率预测。关键优势在于实际运行中的鲁棒性：无需重训即可接入新时间序列数据，且仅凭坐标即可为无观测数据的位置生成预测。在覆盖瑞士127个地点、为期一年的数据集上，模型在预测时长范围内实现6.1%的归一化平均绝对误差，性能媲美商业数值天气预报服务。
原文摘要 · Abstract (English)
Accurate day-ahead forecasts of solar irradiance are required for the large-scale integration of solar photovoltaic (PV) systems into the power grid. However, current forecasting solutions lack the temporal and spatial resolution required by system operators. In this paper, we introduce SolarCrossFormer, a novel deep learning model for day-ahead irradiance forecasting, that combines satellite images and time series from a ground-based network of meteorological stations. SolarCrossFormer uses novel graph neural networks to exploit the inter- and intra-modal correlations of the input data and improve the accuracy and resolution of the forecasts. It generates probabilistic forecasts for any location in Switzerland with a 15-minute resolution for horizons up to 24 hours ahead. One of the key advantages of SolarCrossFormer its robustness in real life operations. It can incorporate new time-series data without retraining the model and, additionally, it can produce forecasts for locations without input data by using only their coordinates. Experimental results over a dataset of one year and 127 locations across Switzerland show that SolarCrossFormer yield a normalized mean absolute error of 6.1 % over the forecasting horizon. The results are competitive with those achieved by a commercial numerical weather prediction service.
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