A novel method for real-time solar generation forecast using weather data, while exploiting both spatial and temporal structural dependencies is proposed. The network observed over time is projected to a lower-dimensional representation where a variety of weather measurements are used to train a structured regression model while weather forecast is used at the inference stage. Experiments were conducted at 288 locations in the San Antonio, TX area on obtained from the National Solar Radiation Database. The model predicts solar irradiance with a good accuracy (R2 0.91 for the summer, 0.85 for the winter, and 0.89 for the global model). The best accuracy was obtained by the Random Forest Regressor. Multiple experiments were conducted to characterize influence of missing data and different time horizons providing evidence that the new algorithm is robust for data missing not only completely at random but also when the mechanism is spatial, and temporal.

翻译：利用天气数据,同时利用空间和时间结构依赖性,提出了实时太阳能发电预测新方法,预计随着时间的推移所观测的网络将具有较低维度的表示法,使用各种气象测量进行结构化回归模型的培训,同时在推断阶段使用天气预报;在国家太阳辐射数据库获得的TX区San Antonio的288个地点进行了实验;该模型预测太阳辐照度的准确性很高(夏季为R2 0.91,冬季为R 0.85,全球模型为0.89)。随机森林回归者获得了最佳的精确度。进行了多次实验,以确定缺失数据的影响和不同时空范围,证明新的算法不仅对随机丢失的数据有效,而且对空间和时间机制缺失的数据也有效。