| description abstract | As wind and solar power play increasingly important roles in the European energy system, unfavorable weather conditions, such as “Dunkelflaute” (extended calm and cloudy periods), will pose ever greater challenges to transmission system operators. Thus, accurate identification and characterization of such events from open data streams (e.g., reanalysis, numerical weather prediction, and climate projection) are going to be crucial. In this study, we propose a two-step, unsupervised deep learning framework [wind and solar network (WISRnet)] to automatically encode spatial patterns of wind speed and insolation, and subsequently, identify Dunkelflaute periods from the encoded patterns. Specifically, a deep convolutional neural network (CNN)–based autoencoder (AE) is first employed for feature extraction from the spatial patterns. These two-dimensional CNN-AE patterns encapsulate both amplitude and spatial information in a parsimonious way. In the second step of the WISRnet framework, a variant of the well-known | |
