Automated Identification of ‘Dunkelflaute’ Events: A Convolutional Neural Network-Based Autoencoder Approach

As wind and solar power play increasingly important roles in the European energy system, unfavourable 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 (named 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 k-means algorithm is used to divide the CNN-AE patterns in region-dependent meteorological clusters. For the validation of the WISRnet framework, aggregated wind and solar power production data from Belgium are used. Using a simple criterion from published literature, all the Dunkelflaute periods are directly identified from this six year-long dataset. Next, each of these periods is associated with a WISRnet-derived cluster. Interestingly, we find that the majority of these Dunkelflaute periods are part of only five clusters (out of twenty five). We show that in lieu of proprietary power production data, the WISRnet framework can identify Dunkelflaute periods from public-domain meteorological data. To further demonstrate the prowess of this framework, it is deployed to identify and characterize Dunkelflaute events in Denmark, Sweden, and the UK.