Electricity grids have become an essential part of daily life, even if they are often not noticed in everyday life. We usually only become particularly aware of this dependence by the time the electricity grid is no longer available. However, significant changes, such as the transition to renewable energy (photovoltaic, wind turbines, etc.) and an increasing number of energy consumers with complex load profiles (electric vehicles, home battery systems, etc.), pose new challenges for the electricity grid. To address these challenges, we propose two first-of-its-kind datasets based on measurements in a broadband powerline communications (PLC) infrastructure. Both datasets FiN-1 and FiN-2, were collected during real practical use in a part of the German low-voltage grid that supplies around 4.4 million people and show more than 13 billion datapoints collected by more than 5100 sensors. In addition, we present different use cases in asset management, grid state visualization, forecasting, predictive maintenance, and novelty detection to highlight the benefits of these types of data. For these applications, we particularly highlight the use of novel machine learning architectures to extract rich information from real-world data that cannot be captured using traditional approaches. By publishing the first large-scale real-world dataset, we aim to shed light on the previously largely unrecognized potential of PLC data and emphasize machine-learning-based research in low-voltage distribution networks by presenting a variety of different use cases.

翻译：电力电网已成为日常生活不可或缺的组成部分，即便在平常生活中人们往往察觉不到其存在。通常只有在电网中断时我们才特别意识到这种依赖性。然而，重大变革——例如向可再生能源（光伏、风力涡轮机等）转型，以及具有复杂负荷特性的能源消费者（电动汽车、家庭电池系统等）数量不断增加——给电网带来了新的挑战。为应对这些挑战，我们提出了两个基于宽带电力线通信（PLC）基础设施测量的首创性数据集。这两个数据集FiN-1和FiN-2是在德国低压电网（为约440万人供电）的实际运行中收集的，包含了由5100多个传感器采集的超过130亿个数据点。此外，我们展示了资产管理、电网状态可视化、预测、预测性维护和异常检测等不同应用场景，以突出这类数据的优势。针对这些应用，我们特别强调了利用新型机器学习架构从真实世界数据中提取丰富信息的能力，这些信息是传统方法无法捕捉的。通过发布首个大规模真实世界数据集，我们旨在揭示PLC数据此前很大程度上未被挖掘的潜力，并通过呈现多种不同应用场景，推动基于机器学习的低压配电网研究。