Innovative Single-Point Sensing for Electrical Event Detection and Classification in Homes
The ElectriSense project explores a novel approach to detecting and classifying electrical events within residential environments. Leveraging electromagnetic interference (EMI) signals from various electrical devices, the study reveals consistent device signatures through rigorous testing in six homes over six months. With achieved accuracies of up to 93.82% using K-Nearest Neighbor techniques, this low-cost solution promotes effective energy monitoring and enhances smart space applications, emphasizing the potential for future developments in ubiquitous computing.
Innovative Single-Point Sensing for Electrical Event Detection and Classification in Homes
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Presentation Transcript
ElectriSense: Single-Point Sensing Using EMI for Electrical Event Detection and Classification in the Homeauthors:Gupta, Reynolds, Patel presenter: Gerritsen
Domain: Activity-inference research
The premise: Detecting electrical events within the home
Prior work: Resistive and inductive electrical loads make detectable noise. Patel, Robertson, Kientz, Reynolds, Abowd, from UbiComp, 2007
Resistive load: No power inrush. Electric heater. Incandescent bulb. Fan.
Inductive load: Power spike inrush. Motor, e.g., hair dryer. Relay, e.g., electromagnet.
New approach: Detecting switch mode power supplies (SMPS)
SMPS High efficiency devices. LCD monitor. Fluorescent bulb. Awkward new washer.
The basic idea: The harmonics of continuous electromagnetic interference
The basic idea: Specific device signatures
Testing: 6 homes, 1 day each Plug in List every appliance Label each device Simulate activity Consistency testing Data storage and output 1 home, 6 months Beep
What they found: K-Nearest Neighbor average accuracy = 91.75% (Clustering pairs 93.82%)
What they found: Single-instance training accuracy = 89.25%
What they found: Device signatures consistent
What they found: Device signatures stable over time
How to improve: Signal separation
How to improve: More refined set of classifiers
How to improve: Reduce vigilance
How to improve: Plug into different phases or the 240 V
Last line from the paper: • Our new strategy shows significant promise as a practical, low-cost solution for providing disaggregating electrical information for energy monitoring and ubiquitous computing applications.
Discussion: • Gamification! • Office use • Smart spaces
