Tree-based Forecasting of Day-ahead Solar Power Generation from Granular Meteorological Features

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.