The importance of wind power as a renewable and cost-efficient power generation technology is growing globally. The impact of wind power on the existing power system, land use, and others over time has been widely studied. Such wind integration studies, especially when they are designed as retrospective bottom-up studies, rely on detailed wind turbine data, including the geographic locations, hub height, and dates of commission. Given the frequency of gaps present in these data sets, basic concepts have been developed to cope with missing data points. In this paper, multiple advanced algorithms were compared with respect to their ability to complete such data sets. One focus was on the selection of predictor variables to analyze the impact of different completion techniques depending on the specific gaps in the data set. A sample application using a German data set indicated that random forests are particularly well suited to the problem at hand.

风力发电作为一种可再生且具有成本效益的发电技术的重要性在全球范围内日益提高。随着时间的流逝，风力对现有电力系统，土地使用及其他方面的影响已得到广泛研究。此类风能集成研究（尤其是当其设计为追溯自下而上的研究时）依赖于详细的风力涡轮机数据，包括地理位置，轮毂高度和投产日期。考虑到这些数据集中出现差距的频率，已经开发出一些基本概念来应对缺失的数据点。在本文中，比较了多种高级算法完成这些数据集的能力。一个重点是选择预测变量，以根据数据集中的特定差距来分析不同完成技术的影响。