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On performance evaluation and machine learning approaches in non-intrusive load monitoring

机译：非侵入式负载监控中的性能评估和机器学习方法

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Non-Intrusive Load Monitoring (NILM) is a set of techniques to gain deep insights into workflows inside buildings based on data provided by smart meters. In this way, the combined consumption needs only to be monitored at a single, central point in the household, providing advantages such as reduced costs for metering equipment. Over the years, a plethora of load monitoring algorithms has been proposed comprising approaches based on Hidden Markov Models (HMM), algorithms based on combinatorial optimisation, and more recently, approaches based on machine learning. However, reproducibility, comparability, and performance evaluation remain open research issues since there is no standardised way researchers evaluate their approaches and report performance. In this paper, the author points out open research issues of performance evaluation in NILM, presents a short survey of deep learning approaches for NILM, and formulates research questions related to open issues in NILM. An outline of future work is given including applied methodology and expected findings.
机译：非侵入式负载监控（NILM）是一组技术，可根据智能电表提供的数据深入了解建筑物内的工作流程。通过这种方式，仅需在家庭的单个中心点监视总消耗量，从而具有诸如降低计量设备成本等优势。多年来，已经提出了许多负载监视算法，包括基于隐马尔可夫模型（HMM）的方法，基于组合优化的算法，以及最近基于机器学习的方法。但是，可重复性，可比性和性能评估仍然是未解决的研究问题，因为没有标准化的方法来研究人员评估其方法并报告性能。在本文中，作者指出了NILM中绩效评估的公开研究问题，对NILM的深度学习方法进行了简短调查，并提出了与NILM中公开问题相关的研究问题。给出了未来工作的概述，包括应用的方法和预期的发现。

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