非侵入式负荷监测中设备建模的用户辅助足迹提取

Roberto Bonfigli, E. Principi, S. Squartini, Marco Fagiani, M. Severini, F. Piazza
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引用次数: 6

摘要

在非侵入式负荷监测(NILM)领域,许多方法都需要一个有监督的电器建模过程,以便向分解算法提供有关电器的信息,并获得与每台电器相关的分解能耗。在许多方法中,设备建模依赖于消耗足迹,这是设备的典型工作周期。由于NILM系统只有可用的总功耗,因此记录的足迹可能会被其他设备损坏,这些设备在此期间无法关闭,例如家庭中的冰箱和冰柜。此外,用户需要一个简化的过程,以便在实际场景中从聚合的功率信号中获得干净的足迹。因此,需要一个用户辅助的足迹提取过程。在这项工作中,该程序被定义为具有两个来源的NILM问题,即所需的器具和冰箱-冰柜组合。该算法的结果分解配置文件之一对应于提取的足迹。然后,将其用于设备建模阶段以创建相应的隐马尔可夫模型(HMM),该模型适用于加性阶乘近似最大后验(AFAMAP)算法。足迹提取程序的有效性是通过对实际问题的分解输出的置信度来评估的,使用从两个不同的数据集(AMPds, ECO)中获取的30天数据。实验使用来自提取的足迹的HMM进行,并与使用来自真实足迹的HMM进行相同问题的置信度(作为设备级消耗)进行比较。结果表明,两种方法的性能具有可比性,最坏的相对F1损失为3.83%,证明了足迹提取方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
User-aided footprint extraction for appliance modelling in Non-Intrusive Load Monitoring
In the area of Non-Intrusive Load Monitoring (NILM), many approaches need a supervised procedure of appliance modelling, in order to provide the informations about the appliances to the disaggregation algorithm and to obtain the disaggregated consumptions related to each one of them. In many approaches, the appliance modelling relies on the consumption footprint, which is a typical working cycle of the appliance. Since the NILM system has only the aggregated power consumption available, the recorded footprint might be corrupted by other appliances, which can not be turned off during this period, i.e., the fridge and freezer in the household. Furthermore, the user needs a facilitated procedure, in order to obtain a clean footprint from the aggregated power signal in real scenario. Therefore, a user-aided footprint extraction procedure is needed. In this work, this procedure is defined as a NILM problem with two sources, i.e., the desired appliance and the fridge-freezer combination. One of the resulting disaggregated profiles of the algorithm corresponds to the extracted footprint. Then, this is used for the appliance modelling stage to create te corresponding Hidden Markov Model (HMM), suitable for the Additive Factorial Approximate Maximum a Posteriori (AFAMAP) algorithm. The effectiveness of the footprint extraction procedure is evaluated through the confidence of the disaggregation output of a real problem, using a span of 30 days data taken from two different datasets (AMPds, ECO). The experiments are conducted using the HMM from the extracted footprint, compared to the confidence of the same problem using the HMM from the true footprint, as appliance level consumption. The results show that the performance are comparable, with the worst relative F1 loss of 3.83%, demonstrating the effectiveness of the footprint extraction procedure.
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