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Adrian Helfenstein

dblp:40/11360 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2012
—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.

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

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Energy systems and smart grids
energy disaggregation
0.112012
Leveraging smart meter data to recognize home appliances · PerCom 2012
Energy systems and smart grids › smart metering
smart meter data analysis
0.112012
Leveraging smart meter data to recognize home appliances · PerCom 2012
Ubiquitous computing and smart environments
smart home
0.012012
Leveraging smart meter data to recognize home appliances · PerCom 2012

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

signature recognition · 0.3signal filtering · 0.3
YearPublicationVenuePosition
2012 Leveraging smart meter data to recognize home appliances
abstract
The worldwide adoption of smart meters that measure and communicate residential electricity consumption gives rise to the development of new energy efficiency services. Several particularly promising applications involve the disaggregation of individual appliances within a particular household in terms of their energy demand. In this paper we present an infrastructure and a set of algorithms that make use of smart meters together with smartphones to realize new energy efficiency services (such as itemized electricity bills or targeted energy saving advice). The smartphones, together with a novel filtering approach, much simplify the training process for appliances signature recognition. We also report on the performance of our system that was tested with 8 simultaneous devices, achieving recognition rates of 87%.
Markus Weiss, Adrian Helfenstein, Friedemann Mattern, Thorsten Staake
PerCom2