开发和评估用于非侵入式负载监控的概率事件检测器

Lucas Pereira
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引用次数: 24

摘要

本文提出并评估了一种用于非侵入式负载监控的概率事件检测算法。与其他概率事件检测器一样,该算法还计算在功率信号的每个样本处发生功率事件的可能性。然而,与之前的算法对事件可能性进行阈值或采用投票方案不同,该算法采用最大值/最小值(即极值)定位器算法来识别潜在的电力事件。针对四个公共数据集对该算法进行了评估,并将其性能与其他四种替代方案进行了比较。结果表明,该算法在四种数据集上具有较强的竞争力。此外,结果还表明,使用极值定位器而不是投票方案,可以提高最先进算法之一的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Developing and evaluating a probabilistic event detector for non-intrusive load monitoring
In this paper we present and evaluate probabilistic event detection algorithm for Non-Intrusive Load Monitoring. Like the other probabilistic event detectors, this algorithm also calculates the likelihood of a power event happening at each sample of the power signal. However, unlike the previous algorithms that threshold or employ voting schemes on the event likelihood, this algorithm employs a maxima/minima (i.e., the extrema) locator algorithm to identify potential power events. The proposed algorithm was evaluated against four public datasets, and its performance was compared to that of other four alternative solutions. The obtained results show that this new algorithm is competitive with the other alternatives in the four datasets. Furthermore, the results also suggest that using an extrema locator instead of a voting scheme, increases the performance of one of the state-of-the art algorithms.
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