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.
翻译:非侵入性负载监测(NILM)是将总电力消耗量分解为各个子组成部分的任务。多年来,信号处理和机器学习算法结合了信息处理和机器学习算法以实现这一目标。大量关于能源分类或NILM的出版物和大量研究工作是针对最佳性能的最先进方法进行的。科学界最初有意利用机器学习工具从数学角度来拟订和描述NILM问题,现已转变为更实用的NILM。现在,我们处于成熟的NILM时期,试图将NILM应用于实际应用情景中。因此,主要感兴趣的是算法的复杂性、可转移性、可靠性、实用性和一般可信赖性。审查缩小早期不成熟的NILM时代与成熟时代之间的差距。特别是,该文件仅对NILM型家用电器方法进行全面文献审查。本文分析、总结和介绍最近出版的大量学术文章的结果。此外,文件讨论了算法、可转移性、可靠性、实用性和可信任性以及一般信任性,这是主要的问题。本文件最后讨论了算法方法的要点,并展示了传统性模型。我们最后需要将这些研究方式和模型介绍。