Non-intrusive load monitoring (NILM) is a technique that efficiently monitors appliances' operational status and energy consumption by utilizing voltage and current data, without intrusive measurements. In NILM, designing efficient classification models and building distinctive load features are crucial. However, due to its continuously variable load characteristics, multi-state load identification remains the most challenging problem in NILM. In this paper, we improve the encoding of the color V–I trajectory by incorporating instantaneous power, thereby enhancing the uniqueness of V–I trajectory features. Furthermore, we investigate a NILM method based on deep learning methods and propose a densely connected convolutional network with squeeze-and-excitation network (SE-DenseNet) architecture to solve the multi-state load identification problem. Initially, the architecture leverages DenseNet's dense connectivity property to generate a multitude of feature maps from the V–I trajectory. Then, SENet's channel attention mechanism is employed to enhance the utilization of effective features, which is more effective for multi-state load identification. Experimental results on the NILM public datasets PLAID and WHITED show that the recognition accuracy of the proposed method reaches 98.60% and 98.88%, respectively, which outperforms most existing methods.

中文翻译：

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非侵入式负载监控中基于颜色编码的 SE-DenseNet 改进多状态电器分类

非侵入式负载监控（NILM）是一种利用电压和电流数据有效监控设备运行状态和能耗的技术，无需侵入式测量。在 NILM 中，设计高效的分类模型和构建独特的负载特征至关重要。然而，由于其连续可变的负载特性，多状态负载识别仍然是NILM中最具挑战性的问题。在本文中，我们通过结合瞬时功率改进了彩色V-I轨迹的编码，从而增强了V-I轨迹特征的唯一性。此外，我们研究了基于深度学习方法的 NILM 方法，并提出了一种具有挤压和激励网络的密集连接卷积网络（SE-DenseNet）架构来解决多状态负载识别问题。最初，该架构利用 DenseNet 的密集连接特性从 V-I 轨迹生成大量特征图。然后，采用SENet的通道注意力机制来增强有效特征的利用率，这对于多状态负载识别更加有效。在NILM公共数据集PLID和WHITED上的实验结果表明，该方法的识别准确率分别达到98.60%和98.88%，优于大多数现有方法。