Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XW, UK.
Sensors (Basel). 2023 May 17;23(10):4845. doi: 10.3390/s23104845.
With the massive, worldwide, smart metering roll-out, both energy suppliers and users are starting to tap into the potential of higher resolution energy readings for accurate billing, improved demand response, improved tariffs better tuned to users and the grid, and empowering end-users to know how much their individual appliances contribute to their electricity bills via nonintrusive load monitoring (NILM). A number of NILM approaches, based on machine learning (ML), have been proposed over the years, focusing on improving the NILM model performance. However, the trustworthiness of the NILM model itself has hardly been addressed. It is important to explain the underlying model and its reasoning to understand why the model underperforms in order to satisfy user curiosity and to enable model improvement. This can be done by leveraging naturally interpretable or explainable models as well as explainability tools. This paper adopts a naturally interpretable decision tree (DT)-based approach for a NILM multiclass classifier. Furthermore, this paper leverages explainability tools to determine local and global feature importance, and design a methodology that informs feature selection for each appliance class, which can determine how well a trained model will predict an appliance on any unseen test data, minimising testing time on target datasets. We explain how one or more appliances can negatively impact classification of other appliances and predict appliance and model performance of the REFIT-data trained models on unseen data of the same house and on unseen houses on the UK-DALE dataset. Experimental results confirm that models trained with the explainability-informed local feature importance can improve toaster classification performance from 65% to 80%. Additionally, instead of one five-classifier approach incorporating all five appliances, a three-classifier approach comprising a kettle, microwave, and dishwasher and a two-classifier comprising a toaster and washing machine improves classification performance for the dishwasher from 72% to 94% and the washing machine from 56% to 80%.
随着全球范围内大规模的智能计量系统的推出，能源供应商和用户都开始利用更高分辨率的能源读数的潜力来实现准确计费、改善需求响应、根据用户和电网情况优化更好的电价、以及通过非侵入式负载监测（NILM）使最终用户了解他们的个人电器对电费的贡献。多年来，已经提出了基于机器学习（ML）的许多 NILM 方法，这些方法主要集中在提高 NILM 模型性能上。然而，NILM 模型本身的可信度几乎没有得到解决。解释底层模型及其推理是很重要的，以便了解模型表现不佳的原因，从而满足用户的好奇心并改进模型。这可以通过利用自然可解释或可解释模型以及解释性工具来实现。本文采用基于自然可解释决策树（DT）的方法来实现 NILM 多类分类器。此外，本文还利用解释性工具来确定局部和全局特征重要性，并设计一种方法，为每个电器类选择特征，这可以确定经过训练的模型在任何未见测试数据上预测电器的效果如何，从而最小化目标数据集中的测试时间。我们解释了一个或多个电器如何对其他电器的分类产生负面影响，并预测在 REFIT 数据上训练的模型在未见数据和 UK-DALE 数据集上未见房屋中的电器和模型性能。实验结果证实，使用具有解释性的局部特征重要性的模型可以将烤面包机的分类性能从 65%提高到 80%。此外，与包含所有五个电器的一个五分类器方法相比，一个由水壶、微波炉和洗碗机组成的三分类器方法和一个由烤面包机和洗衣机组成的两分类器方法，提高了洗碗机的分类性能从 72%到 94%，以及洗衣机的分类性能从 56%到 80%。
