NILMPEds: A Performance Evaluation Dataset for Event Detection Algorithms in Non-Intrusive Load Monitoring

NILMPEds (NILM Performance Evaluation dataset), is a different type of NILM dataset, in a sense that it is aimed primarily at research reproducibility with respect to the development and performance evaluation of event detection algorithms. NILMPEds contains the results of <b>47950</b> event detection models when applied to four public event detection datasets. The different parameter configuration of each model and the ground-truth data are also available. Finally, this dataset also contains the performance evaluation of each model according to <b>31</b> performance metrics.非侵入式负载监测（Non-Intrusive Load Monitoring，简称NILM）性能评估数据集（NILMPEds，NILM Performance Evaluation dataset）是一类独具特色的非侵入式负载监测数据集，其核心目标在于保障事件检测算法开发与性能评估相关研究的可复现性。该数据集收录了将**47950**个事件检测模型应用于四个公开事件检测数据集后的运行结果，同时还提供了各模型的差异化参数配置方案以及真实标签（ground-truth）数据。最后，该数据集还基于**31**项性能指标完成了所有模型的性能评估工作。

搜集汇总

背景与挑战

背景概述

NILMPEds是一个专门用于非侵入式负载监控（NILM）中事件检测算法性能评估的数据集，旨在促进研究可重复性。它包含47950个事件检测模型在四个公共数据集上的结果，每个模型提供参数配置、真实数据以及基于31个性能指标的评估结果，为算法开发和比较提供了全面基准。