Plug loads account for up to one-third of the overall energy use in commercial buildings. There is thus a growing research interest in utilising load monitoring systems to track plug load usage by installing smart plugs to capture high-resolution consumption data. The availability of such data has also enabled the development of automatic plug load identification models that enhance the capabilities of existing load monitoring systems. Through our literature review, we highlighted several limitations that impede real-world implementation, such as the limited number of publicly available datasets for commercial buildings, models trained on data with high sampling frequencies while using an extended time window, and data leakage issues during model training. In this study, we proposed a near-real-time plug load identification approach that uses low-frequency power data (1/60 Hz) to identify plug loads in office spaces. The dataset used in this study is processed by first identifying the active periods of the plug loads before applying a novel dynamic time window strategy during feature extraction. These extracted features are subsequently passed through several classification algorithms and evaluated using different accuracy metrics. The proposed approach is also assessed through multiple experiments, including (1) identifying the best online and offline models, (2) comparing between different time window strategies, and (3) evaluating model performances under different sampling frequencies. As a result, the best online model achieved accuracies up to 93% using the Bagging algorithm with a minimum dynamic time window of 5 minutes. Finally, we highlighted two application areas of automatic plug load identification in energy dashboards and personalised control systems as part of future works.

插头负载占商业建筑总能耗的三分之一。因此，通过安装智能插头来捕获高分辨率的消耗数据，利用负载监视系统来跟踪插头负载的使用，引起了越来越多的研究兴趣。此类数据的可用性还使得能够开发自动插头负载识别模型，从而增强了现有负载监控系统的功能。通过我们的文献综述，我们强调了阻碍现实世界实施的一些限制，例如商业建筑的公共可用数据集数量有限，在使用延长的时间窗口的同时以高采样频率对数据进行训练的模型以及模型期间的数据泄漏问题训练。在这个研究中，我们提出了一种近实时插头负载识别方法，该方法使用低频功率数据（1/60 Hz）来识别办公室空间中的插头负载。通过在特征提取期间应用新颖的动态时间窗口策略之前，先确定插头载荷的活动周期，然后处理本研究中使用的数据集。这些提取的特征随后将通过几种分类算法，并使用不同的精度指标进行评估。还通过多种实验对提出的方法进行了评估，包括（1）确定最佳的在线和离线模型；（2）在不同的时间窗口策略之间进行比较；（3）在不同的采样频率下评估模型的性能。结果是，使用Bagging算法的最佳在线模型的准确度最高可达93％，动态时间窗最少为5分钟。最后，我们重点介绍了能源仪表板和个性化控制系统中自动插头负载识别的两个应用领域，作为未来工作的一部分。