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A time-efficient factorial hidden Semi-Markov model for non-intrusive load monitoring
Electric Power Systems Research ( IF 3.3 ) Pub Date : 2021-05-29 , DOI: 10.1016/j.epsr.2021.107372
Zhao Wu , Chao Wang , Huaiqing Zhang , Wenxiong Peng , Weihua Liu

Hidden Markov Model (HMM) and its variations are widely used in Non-Intrusive Load Monitoring (NILM) due to their relatively small requirement for computing resources and label information. Hidden Semi-Markov Model (HSMM), as one of the most representative models, allows the user to characterize the residual time of a hidden state using a specific probability distribution. Yet, because of its time complexity, the algorithm is not practical in case a large number of appliances are present in the dataset. In this paper, a Time-Efficient Factorial Hidden Semi-Markov Model (TE-FHSMM) is presented to improve the computing efficiency, which is more applicable in real world scenarios. Experiments are conducted on two publicly available datasets and one dataset collected from the laboratory to compare the proposed algorithm with six state-of-the-art NILM algorithms. Results show that the proposed TE-FHSMM model could achieve at least 24.5% reduction in time consumption than the classical Factorial Hidden Semi-Markov Model (FHSMM) while maintaining the performance when dealing with a publicly available dataset with different number of appliances. In addition, experiments on a real world scenario and two publicly available datasets demonstrate that the proposed TE-FHSMM model outperforms six state-of-the-art algorithms in terms of Accuracy and F1 score.



中文翻译:

一种用于非侵入式负载监控的时间效率阶乘隐藏半马尔可夫模型

由于隐马尔可夫模型(HMM)及其变体对计算资源和标签信息的需求相对较小,因此被广泛用于非侵入式负载监控(NILM)。隐藏半马氏模型(HSMM)作为最具代表性的模型之一,允许用户使用特定的概率分布来表征隐藏状态的剩余时间。然而,由于其时间复杂性,在数据集中存在大量设备的情况下,该算法不可行。在本文中,提出了一种时间有效的因子隐半马尔可夫模型(TE-FHSMM)来提高计算效率,它更适用于现实世界的场景。在两个公开可用的数据集和一个从实验室收集的数据集上进行了实验,以将所提出的算法与六种最先进的 NILM 算法进行比较。结果表明,在处理具有不同设备数量的公开可用数据集时,所提出的 TE-FHSMM 模型可以比经典的因子隐半马尔可夫模型 (FHSMM) 减少至少 24.5% 的时间消耗,同时保持性能。此外,在真实世界场景和两个公开可用数据集上的实验表明,所提出的 TE-FHSMM 模型在准确性和 F1 分数方面优于六种最先进的算法。与经典的因子隐半马尔可夫模型 (FHSMM) 相比,时间消耗减少 5%,同时在处理具有不同设备数量的公开可用数据集时保持性能。此外,在真实世界场景和两个公开可用数据集上的实验表明,所提出的 TE-FHSMM 模型在准确性和 F1 分数方面优于六种最先进的算法。与经典的因子隐藏式半马尔可夫模型(FHSMM)相比,其时间消耗减少了5%,同时在处理具有不同设备数量的公共数据集时仍保持了性能。此外,在真实世界场景和两个公开可用数据集上的实验表明,所提出的 TE-FHSMM 模型在准确性和 F1 分数方面优于六种最先进的算法。

更新日期:2021-05-30
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