Load identification is a core concept in non-intrusive load monitoring (NILM). Through NILM systems, users can check their home appliance usage habits and then adjust their behavior to save electricity. In this way, a NILM system offers an effective method to detect the event status of household appliances as well as individual loads’ energy consumption. However, prior NILM methods have encountered a challenge in improving recognition accuracy for both linear load and non-linear load types. These methods used a representative feature, namely transient load signals. However, the transient signals on these loads differ in terms of transient time and transient shape, which is the main cause of reduced accuracy performance in load identification. To this end, this paper presents a novel method, HT-LSTM (Hilbert Transform Long Short-Term Memory), which enhances recognition of the various load types that contain the difference in the transient time and the transient shape of any load signal. The proposed method consists of two main parts: (i) generating a novel transient feature based on a Hilbert transform (HT), called APF (Amplitude-Phase-Frequency). APF features are sequential data, which is used for the classification model; and (ii) applying Sequence-to-Sequence Long Short-Term Memory (Seq2Seq LSTM) to identify appliances by using APF features as the input data. In this work, we evaluate the HT-LSTM method using two high-frequency public datasets, Building-Level fUlly-labeled dataset for Electricity Disaggregation (BLUED) and Plug Load Appliance Identification Dataset (PLAID). Also, we evaluate our method using a private dataset collected in the lab. Based on the experimental results obtained and comparison classification performance pointed, the proposed method outperforms previous methods of F-score measurement on both public datasets in load identification as well as the private dataset.

中文翻译：

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基于希尔伯特变换和序列长短期记忆的负荷识别

负载识别是非侵入式负载监控 (NILM) 的核心概念。通过NILM系统，用户可以查看自己的家电使用习惯，进而调整自己的行为，从而达到省电的目的。通过这种方式，NILM 系统提供了一种有效的方法来检测家用电器的事件状态以及单个负载的能耗。然而，先前的 NILM 方法在提高线性负载和非线性负载类型的识别精度方面遇到了挑战。这些方法使用了一个代表性的特征，即瞬态负载信号。然而，这些负载上的瞬态信号在瞬态时间和瞬态形状方面不同，这是负载识别精度性能降低的主要原因。为此，本文提出了一种新颖的方法，HT-LSTM（Hilbert Transform Long Short-Term Memory），这增强了对各种负载类型的识别，这些负载类型包含任何负载信号的瞬态时间和瞬态形状的差异。所提出的方法由两个主要部分组成：（i）基于希尔伯特变换（HT）生成新的瞬态特征，称为 APF（振幅-相位-频率）。APF特征是序列数据，用于分类模型；(ii) 通过使用 APF 特征作为输入数据，应用序列到序列长短期记忆 (Seq2Seq LSTM) 来识别设备。在这项工作中，我们使用两个高频公共数据集来评估 HT-LSTM 方法，用于电力分解的建筑级完全标记数据集 (BLUED) 和插头负载电器识别数据集 (PLAID)。此外，我们使用实验室收集的私有数据集评估我们的方法。