  A significant fraction (5-15%) of renewable energy generated goes into waste
in the grids around the world today due to oversupply issues and transmission
constraints. Being able to predict when and where renewable curtailment occurs
would improve renewable utilization. The core of this work is to enable the
machine learning community to help decarbonize electricity grids by unlocking
the potential of renewable energy through curtailment prediction.
