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.

用最先进的基于树的机器学习方法来预测光伏发电的日前准确性非常关键，该研究还考虑了气象和天文学因素对光伏发电产量的影响，以及粗粒度和细粒度空间位置的影响。这项研究的结果可以帮助电网运营商、决策者和其他利益相关者优化电网运营、经济调度，并促进分布式光伏发电的整合。