论文标题
使用天气数据和增强梯度的位置，比例和形状模型预测架空分销线路故障
Forecasting overhead distribution line failures using weather data and gradient-boosted location, scale, and shape models
论文作者
论文摘要
架空分配线在分发电力方面起着至关重要的作用，但是它们的独立性使它们容易受到极端天气条件的影响，并导致供应破坏。当前对电力网络的法规意味着先发制人的减轻干扰避免了分销公司的经济损失，从而准确地预测了直接财务重要性的错误预测。在这里，我们介绍了基于梯度提高位置，比例和形状模型为英国网络开发的预测模型，从而根据预测天气条件提供了对故障的时空预测。提出的模型基于（a）树木基础学习者或（b）惩罚平滑和线性的基础学习者 - 导致广义添加剂模型（GAM）结构，后者类别的模型可提供最佳的性能，从而在样本外日志性格上。这些型号已安装到十五年的故障和天气数据中，并显示在多天的预测窗口中提供了良好的准确性，从而为电源恢复提供了切实的支持。
Overhead distribution lines play a vital role in distributing electricity, however, their freestanding nature makes them vulnerable to extreme weather conditions and resultant disruption of supply. The current UK regulation of power networks means preemptive mitigation of disruptions avoids financial penalties for distribution companies, making accurate fault predictions of direct financial importance. Here we present predictive models developed for a UK network based on gradient-boosted location, scale, and shape models, providing spatio-temporal predictions of faults based on forecast weather conditions. The models presented are based on (a) tree base learners or (b) penalised smooth and linear base learners -- leading to a Generalised Additive Model (GAM) structure, with the latter category of models providing best performance in terms of out-of-sample log-likelihood. The models are fitted to fifteen years of fault and weather data and are shown to provide good accuracy over multi-day forecast windows, giving tangible support to power restoration.
