Institute Mihajlo Pupin, University of Belgrade, Volgina 15, 11060 Belgrade, Serbia.
Sensors (Basel). 2023 Jan 28;23(3):1444. doi: 10.3390/s23031444.
Non-intrusive load monitoring (NILM) considers different approaches for disaggregating energy consumption in residential, tertiary, and industrial buildings to enable smart grid services. The main feature of NILM is that it can break down the bulk electricity demand, as recorded by conventional smart meters, into the consumption of individual appliances without the need for additional meters or sensors. Furthermore, NILM can identify when an appliance is in use and estimate its real-time consumption based on its unique consumption patterns. However, NILM is based on machine learning methods and its performance is dependent on the quality of the training data for each appliance. Therefore, a common problem with NILM systems is that they may not generalize well to new environments where the appliances are unknown, which hinders their widespread adoption and more significant contributions to emerging smart grid services. The main goal of the presented research is to apply a domain adversarial neural network (DANN) approach to improve the generalization of NILM systems. The proposed semi-supervised algorithm utilizes both labeled and unlabeled data and was tested on data from publicly available REDD and UK-DALE datasets. The results show a 3% improvement in generalization performance on highly uncorrelated data, indicating the potential for real-world applications.
非侵入式负载监测 (NILM) 考虑了不同的方法来分解住宅、商业和工业建筑的能源消耗，以实现智能电网服务。NILM 的主要特点是，它可以在不需要额外仪表或传感器的情况下，将传统智能电表记录的大块电力需求分解为单个电器的消耗。此外，NILM 可以识别电器何时在使用，并根据其独特的消耗模式估计其实时消耗。然而，NILM 是基于机器学习方法的，其性能取决于每个电器的训练数据的质量。因此，NILM 系统的一个常见问题是，它们可能无法很好地推广到未知电器的新环境中，这阻碍了它们的广泛采用和对新兴智能电网服务的更大贡献。本研究的主要目标是应用域对抗神经网络 (DANN) 方法来提高 NILM 系统的泛化能力。所提出的半监督算法利用了有标签和无标签数据，并在公开可用的 REDD 和 UK-DALE 数据集上进行了测试。结果表明，在高度不相关的数据上，泛化性能提高了 3%，这表明了在实际应用中的潜力。
