Abstract
Numerous approaches exist for disaggregating power consumption data, referred to as non-intrusive load monitoring (NILM). Whereas NILM is primarily used for energy monitoring, we intend to disaggregate a household’s power consumption to detect human activity in the residence. Therefore, this paper presents a novel approach for NILM, which uses pattern recognition on the raw power waveform of the smart meter measurements to recognize individual household appliance actions. The presented NILM approach is capable of (near) real-time appliance action detection in a streaming setting, using edge computing. It is unique in our approach that we quantify the disaggregating uncertainty using continuous pattern correlation instead of binary device activity states. Further, we outline using the disaggregated appliance activity data for human activity recognition (HAR). To evaluate our approach, we use a dataset collected from actual households. We show that the developed NILM approach works, and the disaggregation quality depends on the pattern selection and the appliance type. In summary, we demonstrate that it is possible to detect human activity within the residence using a motif-detection-based NILM approach applied to smart meter measurements.
Highlights
To provide residents with feedback about the power consumption of the appliances in their households, and to motivate them to save energy, numerous organizations and researchers are working on methods to monitor household power consumption, in detail, at the appliance level [1,2,3,4]
The remaining paper is structured as follows: we review the non-intrusive load monitoring (NILM) literature in Section 2 and outline how NILM is already used in the human activity recognition (HAR) context
We introduce a new approach suitable for processing aggregated data, measured by commercial smart meters in real-time to detect typical interactions with electrical appliances that are related to human activity
Summary
To provide residents with feedback about the power consumption of the appliances in their households, and to motivate them to save energy, numerous organizations and researchers are working on methods to monitor household power consumption, in detail, at the appliance level [1,2,3,4] These methods are called Appliance Load Monitoring (ALM). One is Intrusive Load Monitoring (ILM), which means that each appliance is monitored individually by sensing, for example, with the help of individual power plugs at each appliance This method provides precise data, but the installation of such a system is complex and expensive [2,4]. Due to many different appliances that can be present in a household, and the overlapping of different appliances in the total power consumption, it is still not possible to fully disaggregate the total power consumption of a household [1,2,3,4,5]
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