Non-intrusive load monitoring (NILM) can identify each electrical load and its operating state in a household by using the voltage and current data measured at a single point on the bus, thereby behaving as a key technology for smart grid construction and effective energy consumption. The existing NILM methods mainly focus on the identification of pre-trained loads, which can achieve high identification accuracy and satisfying outcomes. However, unknown load identification is rarely involved among those methods and the scalability of NILM is still a crucial problem at the current stage. In light of this, we have proposed a non-intrusive load identification method based on a Siamese network, which can be retrained after the detection of an unknown load to increase the identification accuracy for unknown loads. The proposed Siamese network comprises a fixed convolutional neural network (CNN) and two retrainable back propagation (BP) networks. When an unknown load is detected, the low-dimensional features of its voltage–current (V-I) trajectory are extracted by using the fixed CNN model, and the BP networks are retrained online. The finetuning of BP network parameters through retraining can improve the representation ability of the network model; thus, a high accuracy of unknown load identification can be achieved by updating the Siamese network in real time. The public WHITED and PLAID datasets are used for the validation of the proposed method. Finally, the practicality and scalability of the method are demonstrated using a real-house environment test to prove the ability of online retraining on an embedded Linux system with STM32MP1 as the core.

中文翻译：

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基于可再训练孪生网络的非侵入式负载识别

非侵入式负荷监测（NILM）可以通过母线上单点测量的电压和电流数据来识别家庭中的每个电力负荷及其运行状态，从而成为智能电网建设和有效能源消耗的关键技术。现有的NILM方法主要集中于预训练负载的识别，可以实现较高的识别精度和令人满意的结果。然而，这些方法很少涉及未知负载识别，NILM的可扩展性仍然是现阶段的一个关键问题。鉴于此，我们提出了一种基于Siamese网络的非侵入式负载识别方法，该方法可以在检测到未知负载后进行重新训练，以提高未知负载的识别精度。所提出的 Siamese 网络包括一个固定的卷积神经网络（CNN）和两个可重新训练的反向传播（BP）网络。当检测到未知负载时，使用固定的CNN模型提取其电压-电流（VI）轨迹的低维特征，并对BP网络进行在线重新训练。通过再训练对BP网络参数进行微调，可以提高网络模型的表示能力；因此，通过实时更新Siamese网络可以实现高精度的未知负载识别。公共 WHITED 和 PLAID 数据集用于验证所提出的方法。最后通过真实环境测试证明了该方法的实用性和可扩展性，证明了在以STM32MP1为核心的嵌入式Linux系统上在线再训练的能力。