Weather data modelling for energy system models using machine learning

Weather data is essential to model and optimise energy systems, which are based on high shares of renewable generation sources. However, differences between data sources can be significant and often little emphasis is put on energy-related variables such as hub-height wind speeds.

In this work, we use generative adversarial networks (GAN), a class of machine learning systems, to model weather data for large-scale energy system models and optimise energy systems of different scales and sizes.

We show that generating weather data using GAN saves effort as required for processing large amounts of weather data and that it can reliably reproduce results from using weather data produced by numerical models.