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.
A lot of publications and extensive research works are performed on energy
disaggregation or NILM for the state-of-the-art methods to reach on the
desirable 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. Nowadays, we are in the
mature NILM period where there is an attempt for NILM to be applied in
real-life application scenarios. Thus, 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. Also, 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.

该研究综述了近期针对住宅电器的非侵入式负载监测方法，分析总结了大量发表的学术文章，讨论了方法的亮点和应用问题，并强调了传统分解模型向实际可信框架的转化的必要性。