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.

翻译：准确的日前光伏发电功率预测对于支持当地电网高光伏渗透率及保障电网稳定性至关重要。本研究采用先进的树模型机器学习方法进行预测，与既往研究不同，我们同时考虑了以下因素：（i）多种气象及天文特征对光伏发电功率的影响，（ii）这些特征在粗粒度与细粒度空间位置上的差异。为此，我们利用比利时数据对日前光伏发电功率进行逐小时分辨率预测。研究结论可协助电力公司、决策者及其他利益相关方优化电网调度与经济运行，促进分布式光伏发电并入电网。