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Cameron Pickett

dblp:151/0196 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2014
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Ubiquitous computing and smart environments · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Energy systems and smart grids · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Ubiquitous computing and smart environments › smart buildings
energy monitoring
0.212014
A self-calibrating approach to whole-home contactless power consumption sensing · UbiComp 2014
Energy systems and smart grids › building energy management
home energy management
0.112014
A self-calibrating approach to whole-home contactless power consumption sensing · UbiComp 2014

Methods — techniques the papers use, named apart from their topics

self-calibration · 0.4neural network · 0.4
YearPublicationVenuePosition
2014 A self-calibrating approach to whole-home contactless power consumption sensing
abstract
In this paper, we present a significant improvement over past work on non-contact end-user deployable sensor for real time whole home power consumption. The technique allows users to place a single device consisting of magnetic pickups on the outside of a power or breaker panel to infer whole home power consumption without the need for professional installation of current transformers (CTs). The new approach does not require precise placement on the breaker panel, a key requirement in previous approaches. This is enabled through a self-calibration technique using a neural network that dynamically learns the transfer function despite the placement of the sensor and the construction of the breaker panel itself. We also demonstrate the ability to actually infer true power using this technique, unlike past solutions that have only been able to capture apparent power. We have evaluated our technique in six homes and one industrial building, including one seven-day deployment. Our results show we can estimate true power consumption with an average accuracy of 95.0% during naturalistic energy use in the home.
Md Tanvir Islam Aumi, Sidhant Gupta, Cameron Pickett, Matthew S. Reynolds, Shwetak N. Patel
UbiComp3