使用过滤管道、概率背包和标记分区映射的通用非侵入式负载监控（UNILM）
Universal Non-Intrusive Load Monitoring (UNILM) Using Filter Pipelines, Probabilistic Knapsack, and Labelled Partition Maps
摘要：
能够在不需要传感器的情况下跟踪电器的能源使用情况，可以帮助居住者减少能源消耗，从而在节省资金的同时帮助保护环境。非侵入式负载监控（NILM）就是要做到这一点。NILM面临的最困难的问题之一是能够在无人监督的情况下运行-在没有先验知识的情况下发现设备-并且独立于不同国家和地区的设备组合和操作特征的差异而运行。我们提出了一种解决方案，可以通过使用先进的过滤器管道来预处理数据，使用概率背包算法来分解聚合智能电表信号的高斯家电模型，以及分区地图来标记发现的家电以及它们使用的能源数量，无论国家/地区如何。实验结果表明，相对复杂的家电信号可以跟踪占总的总能量消耗的93.7%。
英文摘要：
Being able to track appliances energy usage without the need of sensors can help occupants reduce their energy consumption to help save the environment all while saving money. Non-intrusive load monitoring (NILM) tries to do just that. One of the hardest problems NILM faces is the ability to run unsupervised -- discovering appliances without prior knowledge -- and to run independent of the differences in appliance mixes and operational characteristics found in various countries and regions. We propose a solution that can do this with the use of an advanced filter pipeline to preprocess the data, a Gaussian appliance model with a probabilistic knapsack algorithm to disaggregate the aggregate smart meter signal, and partition maps to label which appliances were found and how much energy they use no matter the country/region. Experimental results show that relatively complex appliance signals can be tracked accounting for 93.7% of the total aggregate energy consumed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
