As renewable energy generation increases, the impacts of weather and climate on energy generation and demand become critical to the reliability of the energy system. However, these impacts are often overlooked. Global climate models (GCMs) can be used to understand possible changes to our climate, but their coarse resolution makes them difficult to use in energy system modelling. Here we present open-source generative machine learning methods that produce meteorological data at a nominal spatial resolution of 4 km at an hourly frequency based on inputs from 100 km daily-average GCM data. These methods run 40 times faster than traditional downscaling methods and produce data that have high-resolution spatial and temporal attributes similar to historical datasets. We demonstrate that these methods can be used to downscale projected changes in wind, solar and temperature variables across multiple GCMs including projections for more frequent low-wind and high-temperature events in the Eastern United States.

随着可再生能源发电量的增加，天气和气候对能源发电和需求的影响对于能源系统的可靠性变得至关重要。然而，这些影响常常被忽视。全球气候模型（GCM）可用于了解气候可能发生的变化，但其粗糙分辨率使其难以用于能源系统建模。在这里，我们提出了开源生成机器学习方法，该方法根据 100 公里日均 GCM 数据的输入，以每小时 4 公里的名义空间分辨率生成气象数据。这些方法的运行速度比传统降尺度方法快 40 倍，并生成具有与历史数据集类似的高分辨率空间和时间属性的数据。我们证明，这些方法可用于缩小多个 GCM 中风、太阳和温度变量的预计变化，包括对美国东部更频繁的低风和高温事件的预测。