Fine-grained appliance usage and energy monitoring through mobile and power-line sensing

Abstract

To promote energy-efficient operations in residential and office buildings, non-intrusive load monitoring (NILM) techniques have been proposed to infer the fine-grained power consumption and usage patterns of appliances from power-line measurement data. Fine-grained monitoring of everyday appliances (such as toasters and coffee makers) can not only promote energy-efficient building operations, but also provide unique insights into the context and activities of individuals. Current building-level NILM techniques are unable to identify the consumption characteristics of relatively low-load appliances, whereas smart-plug based solutions incur significant deployment and maintenance costs. In this paper, we investigate an intermediate architecture, where smart circuit breakers provide measurements of aggregate power consumption at room (or section) level granularity. We then investigate techniques to identify the usage and energy consumption of individual appliances from such measurements. We first develop a novel correlation-based approach called CBPA to identify individual appliances based on both their unique transient and steady-state power signatures. While promising, CBPA fails when the set of candidate appliances is too large. To further improve the accuracy of appliance level usage estimation, we then propose a hybrid system called AARPA, which uses mobile sensing to first infer high-level activities of daily living (ADLs), and then uses knowledge of such ADLs to effectively reduce the set of candidate appliances that potentially contribute to the aggregate readings at any point. We evaluate two variants of this algorithm, and show, using real-life data traces gathered from 10 domestic users, that our fusion of mobile and power-line sensing is very promising: it identified all devices that were used in each data trace, and it identified the usage duration and energy consumption of low-load consumer appliances with ∼ 87% accuracy.

An Optimal Load Disaggregation Method Based on Power Consumption Pattern for Low Sampling Data

“…However, there are still errors in identifying the appliances with similar PQ characteristics when only low-frequency power and active power are used. In [22], the authors analyzed monitored power data and found that the power consumption pattern of appliances was useful in identifying them. The power sequences of typical household appliances from AMPds are extracted, and the power consumption curves in Figure 2 are provided to illustrate their power consumption pattern.…”

Longitudinal energy waste detection with visualization

“…The applicability of using user WiFi data to localize appliance usages is discussed in [13]. In a similar vein location and activity has been used in [14] to reduce the search space of disaggregation from contextual information.…”

Monitoring the Well-being of Older People by Energy Usage Patterns: Systematic Review of the Literature and Evidence Synthesis

“…Based on the disaggregation of energy usage, ADL patterns can be established in a simple, unobtrusive, and inexpensive way [ 19 ]. By disaggregating the total energy load, it is possible to determine which appliances are being used on a certain day [ 20 - 23 ]. This technique, also known as nonintrusive load monitoring, makes it possible, by using algorithms, to infer the fine-grained energy usage patterns of different appliances in the household [ 20 , 24 - 26 ].…”

“…By disaggregating the total energy load, it is possible to determine which appliances are being used on a certain day [ 20 - 23 ]. This technique, also known as nonintrusive load monitoring, makes it possible, by using algorithms, to infer the fine-grained energy usage patterns of different appliances in the household [ 20 , 24 - 26 ]. This energy usage pattern could be linked to health-related activities, such as cooking, which can therefore be used as a proxy for the general health and safety of the persons living in this household and possibly anticipate accidents or hospitalization [ 27 ].…”