In recent years, smart meters have been widely adopted by electricity
suppliers to improve the management of the smart grid system. These meters
usually collect energy consumption data at a very low frequency (every 30min),
enabling utilities to bill customers more accurately. To provide more
personalized recommendations, the next step is to detect the appliances owned
by customers, which is a challenging problem, due to the very-low meter reading
frequency. Even though the appliance detection problem can be cast as a time
series classification problem, with many such classifiers having been proposed
in the literature, no study has applied and compared them on this specific
problem. This paper presents an in-depth evaluation and comparison of
state-of-the-art time series classifiers applied to detecting the
presence/absence of diverse appliances in very low-frequency smart meter data.
We report results with five real datasets. We first study the impact of the
detection quality of 13 different appliances using 30min sampled data, and we
subsequently propose an analysis of the possible detection performance gain by
using a higher meter reading frequency. The results indicate that the
performance of current time series classifiers varies significantly. Some of
them, namely deep learning-based classifiers, provide promising results in
terms of accuracy (especially for certain appliances), even using 30min sampled
data, and are scalable to the large smart meter time series collections of
energy consumption data currently available to electricity suppliers.
Nevertheless, our study shows that more work is needed in this area to further
improve the accuracy of the proposed solutions. This paper was accepted in
e-Energy '23.

本文研究了时序分类器在检测低频智能电表数据中多种家电的存在和缺失方面的应用和比较，并提高了关于提高检测性能的建议。

利用超低频智能电表时间序列进行家电检测

Appliance Detection Using Very Low-Frequency Smart Meter Time Series

Modern time series classifiers display impressive predictive capabilities,
yet their decision-making processes mostly remain black boxes to the user. At
the same time, model-agnostic explainers, such as the recently proposed SHAP,
promise to make the predictions of machine learning models interpretable,
provided there are well-designed domain mappings. We bring both worlds together
in our timeXplain framework, extending the reach of explainable artificial
intelligence to time series classification and value prediction. We present
novel domain mappings for the time and the frequency domain as well as series
statistics and analyze their explicative power as well as their limits. We
employ timeXplain in a large-scale experimental comparison of several
state-of-the-art time series classifiers and discover similarities between
seemingly distinct classification concepts such as residual neural networks and
elastic ensembles.

该研究提出了一种名为 timeXplain 的框架，旨在将解释性人工智能应用于时间序列分类和价值预测中，并在该框架下对几种最先进的时间序列分类器进行了大规模实验比较，发现了似乎不同的分类概念之间的相似性。