Near-real-time plug load identification using low-frequency power data in office spaces: Experiments and applications

Abstract 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.