论文标题
使用开源数据进行无监督评估，以了解深度学习的分布式PV映射的准确性
Towards unsupervised assessment with open-source data of the accuracy of deep learning-based distributed PV mapping
论文作者
论文摘要
光伏（PV）能量正在迅速生长，是减轻能源危机的关键。但是，分布式PV的生成（占PV已安装容量的一半）通常不可用传输系统操作员（TSO），这使得平衡负载和供应并避免电网拥塞变得越来越困难。为了评估分布式PV生成，TSO需要有关分布式PV安装元数据的精确知识。近年来，已经提出了许多基于遥感的方法来绘制这些装置。但是，必须在工业过程中使用这些方法，以评估其对映射区域的准确性，即部署过程中模型所涵盖的区域。我们将下游任务精度（DTA）定义为映射区域的精度，使用公开可用的数据源和模型的输出自动计算，并以可解释的方式对操作员表示。我们基于现有的模型用于分布式PV映射，并显示它们在DTA方面的性能。我们表明，在测试集上计算的准确性高估了映射区域的精度约30个百分点。我们的方法为将基于深度学习的管道更安全地集成用于远程光伏映射铺平了道路。代码可从https://github.com/gabrielkasmi/deeppvmapper获得。
Photovoltaic (PV) energy is rapidly growing and key to mitigating the energy crisis. However, distributed PV generation, which amounts to half of the PV installed capacity, is typically unavailable to transmission system operators (TSOs), making it increasingly difficult to balance the load and supply and avoid grid congestions. To assess distributed PV generation, TSOs need precise knowledge regarding the metadata of distributed PV installations. Many remote sensing-based approaches have been proposed to map these installations in recent years. However, to use these methods in industrial processes, assessing their accuracy over the mapping area, i.e., the area covered by the model during deployment, is necessary. We define the downstream task accuracy (DTA) as the accuracy over the mapping area, automatically computed using publicly available data sources and the model's outputs and expressed in an interpretable way for operators. We benchmark existing models for distributed PV mapping and show how they perform in terms of DTA. We show that the accuracy computed on the test set overestimates by about 30 percentage points the accuracy on the mapping area. Our approach paves the way for safer integration of deep-learning-based pipelines for remote PV mapping. Code is available at https://github.com/gabrielkasmi/deeppvmapper.
