论文标题
基于太阳生成预测的空间和时间嵌入的太阳辐射预测
Prediction of Solar Radiation Based on Spatial and Temporal Embeddings for Solar Generation Forecast
论文作者
论文摘要
提出了一种使用天气数据实时太阳生成预测的新方法，同时提出了利用空间和时间结构依赖性。随着时间的流逝，观察到的网络被预测到较低维度的表示，在该表示的情况下，在推理阶段使用天气预报时，使用各种天气测量来训练结构化回归模型。从国家太阳辐射数据库获得的德克萨斯州圣安东尼奥市的288个地点进行了实验。该模型预测具有良好精度的太阳辐照度（夏季R2 0.91，冬季为0.85，全球模型为0.89）。随机森林回归者获得了最佳准确性。进行了多个实验，以表征缺失数据的影响和不同的时间范围的影响，证明新算法不仅完全随机，而且在机制是空间和时间上都丢失的数据是可靠的。
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.
