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.

翻译：将太阳能光伏（PV）系统大规模接入电网需要精确的日前太阳辐照度预测。然而，现有预测方案在时空分辨率上难以满足系统运营商的需求。本文提出SolarCrossFormer——一种融合卫星影像与地面气象站时间序列数据的深度学习模型，用于日前辐照度预测。该模型采用新型图神经网络，通过挖掘输入数据的模态间与模态内关联性，提升了预测的精度与分辨率。该模型能以15分钟分辨率生成瑞士境内任意位置未来24小时的概率预测。SolarCrossFormer的关键优势之一在于其在实际运行中的鲁棒性：无需重新训练模型即可整合新的时间序列数据，且仅需目标位置坐标即可对无输入数据的区域生成预测。基于瑞士127个地点一年数据集的实验表明，SolarCrossFormer在整个预测范围内的归一化平均绝对误差为6.1%，其预测结果与商业数值天气预报服务的性能相当。