School of Rural and Surveying Engineering, National Technical University of Athens, 15773 Athens, Greece.
School of Electrical and Computer Engineering, National Technical University of Athens, 15773 Athens, Greece.
Sensors (Basel). 2022 Aug 5;22(15):5872. doi: 10.3390/s22155872.
Non-intrusive load monitoring (NILM) is the task of disaggregating the total power consumption into its individual sub-components. Over the years, signal processing and machine learning algorithms have been combined to achieve this. Many publications and extensive research works are performed on energy disaggregation or NILM for the state-of-the-art methods to reach the desired performance. The initial interest of the scientific community to formulate and describe mathematically the NILM problem using machine learning tools has now shifted into a more practical NILM. Currently, we are in the mature NILM period where there is an attempt for NILM to be applied in real-life application scenarios. Thus, the complexity of the algorithms, transferability, reliability, practicality, and, in general, trustworthiness are the main issues of interest. This review narrows the gap between the early immature NILM era and the mature one. In particular, the paper provides a comprehensive literature review of the NILM methods for residential appliances only. The paper analyzes, summarizes, and presents the outcomes of a large number of recently published scholarly articles. Furthermore, the paper discusses the highlights of these methods and introduces the research dilemmas that should be taken into consideration by researchers to apply NILM methods. Finally, we show the need for transferring the traditional disaggregation models into a practical and trustworthy framework.
非侵入式负载监测 (NILM) 的任务是将总功耗分解为其各个子分量。多年来，信号处理和机器学习算法已被结合使用以实现这一目标。许多出版物和广泛的研究工作都针对能源分解或 NILM 进行，以达到最新方法的预期性能。科学界最初的兴趣是使用机器学习工具来制定和描述 NILM 问题，现在已经转移到更实际的 NILM 上。目前，我们正处于成熟的 NILM 阶段，正在尝试将 NILM 应用于现实生活中的应用场景。因此，算法的复杂性、可转移性、可靠性、实用性以及总体上的可信度是主要关注点。这篇综述缩小了早期不成熟的 NILM 时代和成熟时代之间的差距。特别是，本文对仅用于住宅电器的 NILM 方法进行了全面的文献综述。本文分析、总结和呈现了大量最近发表的学术文章的结果。此外，本文还讨论了这些方法的重点，并介绍了研究人员在应用 NILM 方法时应考虑的研究难题。最后，我们表明需要将传统的分解模型转移到实际和可信的框架中。
