  Electricity grids have become an essential part of daily life, even if they
are often not noticed in everyday life. We usually only become particularly
aware of this dependence by the time the electricity grid is no longer
available. However, significant changes, such as the transition to renewable
energy (photovoltaic, wind turbines, etc.) and an increasing number of energy
consumers with complex load profiles (electric vehicles, home battery systems,
etc.), pose new challenges for the electricity grid. To address these
challenges, we propose two first-of-its-kind datasets based on measurements in
a broadband powerline communications (PLC) infrastructure. Both datasets FiN-1
and FiN-2, were collected during real practical use in a part of the German
low-voltage grid that supplies around 4.4 million people and show more than 13
billion datapoints collected by more than 5100 sensors. In addition, we present
different use cases in asset management, grid state visualization, forecasting,
predictive maintenance, and novelty detection to highlight the benefits of
these types of data. For these applications, we particularly highlight the use
of novel machine learning architectures to extract rich information from
real-world data that cannot be captured using traditional approaches. By
publishing the first large-scale real-world dataset, we aim to shed light on
the previously largely unrecognized potential of PLC data and emphasize
machine-learning-based research in low-voltage distribution networks by
presenting a variety of different use cases.
