Tree-based Forecasting of Day-ahead Solar Power Generation from Granular
  Meteorological Features
Nick Berlanger Noah van Ophoven Tim Verdonck Ines Wilms
Accurate forecasts for day-ahead photovoltaic (PV) power generation are
crucial to support a high PV penetration rate in the local electricity grid and
to assure stability in the grid. We use state-of-the-art tree-based machine
learning methods to produce such forecasts and, unlike previous studies, we
hereby account for (i) the effects various meteorological as well as
astronomical features have on PV power production, and this (ii) at coarse as
well as granular spatial locations. To this end, we use data from Belgium and
forecast day-ahead PV power production at an hourly resolution. The insights
from our study can assist utilities, decision-makers, and other stakeholders in
optimizing grid operations, economic dispatch, and in facilitating the
integration of distributed PV power into the electricity grid.