Non-intrusive load monitoring (NILM) is a technique which extracts individual appliance consumption and operation state change information from the aggregate power consumption made by a single residential or commercial unit. NILM plays a pivotal role in modernizing building energy management by disaggregating total energy consumption into individual appliance-level insights. This enables informed decision-making, energy optimization, and cost reduction. However, NILM encounters substantial challenges like signal noise, data availability, and data privacy concerns, necessitating advanced algorithms and robust methodologies to ensure accurate and secure energy disaggregation in real-world scenarios. Deep learning techniques have recently shown some promising results in NILM research, but training these neural networks requires significant labeled data. Obtaining initial sets of labeled data for the research by installing smart meters at the end of consumers’ appliances is laborious and expensive and exposes users to severe privacy risks. It is also important to mention that most NILM research uses empirical observations instead of proper mathematical approaches to obtain the threshold value for determining appliance operation states (On/Off) from their respective energy consumption value. This paper proposes a novel semi-supervised multilabel deep learning technique based on temporal convolutional network (TCN) and long short-term memory (LSTM) for classifying appliance operation states from labeled and unlabeled data. The two thresholding techniques, namely Middle-Point Thresholding and Variance-Sensitive Thresholding, which are needed to derive the threshold values for determining appliance operation states, are also compared thoroughly. The superiority of the proposed model, along with finding the appliance states through the Middle-Point Thresholding method, is demonstrated through 15% improved overall improved F1_micro score and almost 26% improved Hamming loss, F1 and Specificity score for the performance of individual appliance when compared to the benchmarking techniques that also used semi-supervised learning approach.

非侵入式负载监控（NILM）是一种从单个住宅或商业单位的总功耗中提取单个设备功耗和运行状态变化信息的技术。NILM 通过将总能源消耗分解为单个设备级别的见解，在现代化建筑能源管理方面发挥着关键作用。这使得明智的决策、能源优化和成本降低成为可能。然而，NILM 遇到了信号噪声、数据可用性和数据隐私问题等重大挑战，需要先进的算法和强大的方法来确保在现实场景中准确、安全的能量分解。深度学习技术最近在 NILM 研究中显示出一些有希望的结果，但训练这些神经网络需要大量的标记数据。通过在消费者电器末端安装智能电表来获取用于研究的初始标记数据集既费力又昂贵，并且使用户面临严重的隐私风险。还值得一提的是，大多数 NILM 研究使用经验观察而不是适当的数学方法来获取阈值，用于根据各自的能耗值确定电器操作状态（开/关）。本文提出了一种基于时间卷积网络（TCN）和长短期记忆（LSTM）的新型半监督多标签深度学习技术，用于从标记和未标记数据中对电器操作状态进行分类。还对导出确定电器操作状态的阈值所需的两种阈值技术，即中点阈值和方差敏感阈值进行了彻底的比较。所提出的模型的优越性，以及通过中点阈值方法找到电器状态，通过整体改进的 F1_微分数提高了 15%，以及单个电器性能的汉明损失、F1 和特异性分数提高了近 26%，证明了该模型的优越性与也使用半监督学习方法的基准测试技术相比。