Strong winds induced by extratropical storms cause a large number of power outages, especially in highly forested countries such as Finland. Thus, predicting the impact of the storms is one of the key challenges for power grid operators. This article introduces a novel method to predict the storm severity for the power grid employing ERA5 reanalysis data combined with forest inventory. We start by identifying storm objects from wind gust and pressure fields by using contour lines of 15 m s⁻¹ and 1000 hPa, respectively. The storm objects are then tracked and characterized with features derived from surface weather parameters and forest vegetation information. Finally, objects are classified with a supervised machine-learning method based on how much damage to the power grid they are expected to cause. Random forest classifiers, support vector classifiers, naïve Bayes processes, Gaussian processes, and multilayer perceptrons were evaluated for the classification task, with support vector classifiers providing the best results.

中文翻译：

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预测由温带风暴引起的停电

温带风暴引起的强风导致大量停电，特别是在森林茂密的国家，例如芬兰。因此，预测风暴的影响是电网运营商面临的主要挑战之一。本文介绍了一种使用ERA5重新分析数据结合森林清单来预测电网风暴严重性的新方法。我们首先使用15 m s ^-1的等高线从阵风和压力场中识别风暴对象和1000 hPa。然后使用从地表天气参数和森林植被信息得出的特征来跟踪和表征风暴对象。最后，根据预期对对象电网造成的损害，使用监督式机器学习方法对对象进行分类。对随机森林分类器，支持向量分类器，朴素贝叶斯过程，高斯过程和多层感知器进行了分类任务评估，其中支持向量分类器提供了最佳结果。