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Investigating the performance gap between testing on real and denoised aggregates in non-intrusive load monitoring

机译：在非侵入式负荷监测中调查真实和去噪骨料测试的性能差距

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Prudent and meaningful performance evaluation of algorithms is essential for the progression of any research field. In the field of Non-Intrusive Load Monitoring (NILM), performance evaluation can be conducted on real-world aggregate signals, provided by smart energy meters or artificial superpositions of individual load signals (i.e., denoised aggregates). It has long been suspected that testing on these denoised aggregates provides better evaluation results mainly due to the fact that the signal is less complex. Complexity in real-world aggregate signals increases with the number of unknown/untracked loads. Although this is a known performance reporting problem, an investigation into the actual performance gap between real and denoised testing is still pending. In this paper, we examine the performance gap between testing on real-world and denoised aggregates with the aim of bringing clarity into this matter. Starting with an assessment of noise levels in datasets, we find significant differences in test cases. We give broad insights into our evaluation setup comprising three load disaggregation algorithms, two of them relying on neural network architectures. The results presented in this paper, based on studies covering three scenarios with ascending noise levels, show a strong tendency towards load disaggregation algorithms providing significantly better performance on denoised aggregate signals. A closer look at the outcome of our studies reveals that all appliance types could be subject to this phenomenon. We conclude the paper by discussing aspects that could be causing these considerable gaps between real and denoised testing in NILM.
机译：算法和有意义的算法表现评估对于任何研究领域的进展至关重要。在非侵入式负荷监测（NILM）领域中，性能评估可以在真实世界的聚合信号上进行，由智能能量计或单个负载信号的人工叠加提供（即，去噪聚集体）。已经暂时怀疑这些去噪聚集体的测试提供了更好的评估结果，这主要是由于信号更易于复杂的事实。实际聚合信号中的复杂性随着未知/未触发的负载的数量而增加。虽然这是一个已知的表现报告问题，但对实际和去噪测试之间的实际性能差距的调查仍在等待。在本文中，我们研究了现实世界的测试与去噪综合的表现差距，目的是将清晰度带入此事。从数据集中的噪声水平进行评估开始，我们发现测试用例的显着差异。我们向我们的评估设置提供了广泛的见解，包括三个负载分组算法，其中两个依赖于神经网络架构。本文提出的结果，基于涵盖三种具有升高噪声水平的方案的研究，表明了负载分解算法的强烈趋势，为去噪聚集信号提供明显更好的性能。仔细看看我们的研究结果表明，所有器具类型都可能受到这种现象的影响。我们通过讨论可能导致尼尔的真实和去噪测试之间的这些相当大的间隙的方面来结束论文。

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