Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Brian Holben

dblp:75/8919 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
0since 2021 · last 2010
—ORCID · none

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

Computer networks · 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 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Ubiquitous computing and smart environments
smart home
0.112010
The smart thermostat: using occupancy sensors to save energy in homes · SenSys 2010
Ubiquitous computing and smart environments › smart home
smart thermostat
0.112010
The smart thermostat: using occupancy sensors to save energy in homes · SenSys 2010
Energy systems and smart grids › building energy management
home energy management
0.012010
The smart thermostat: using occupancy sensors to save energy in homes · SenSys 2010
Energy systems and smart grids › building energy management
HVAC control
0.012010
The smart thermostat: using occupancy sensors to save energy in homes · SenSys 2010

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

occupancy sensing · 0.2
YearPublicationVenuePosition
2010 The smart thermostat: using occupancy sensors to save energy in homes
abstract
Heating, ventilation and cooling (HVAC) is the largest source of residential energy consumption. In this paper, we demonstrate how to use cheap and simple sensing technology to automatically sense occupancy and sleep patterns in a home, and how to use these patterns to save energy by automatically turning off the home's HVAC system. We call this approach the smart thermostat. We evaluate this approach by deploying sensors in 8 homes and comparing the expected energy usage of our algorithm against existing approaches. We demonstrate that our approach will achieve a 28% energy saving on average, at a cost of approximately $25 in sensors. In comparison, a commercially-available baseline approach that uses similar sensors saves only 6.8% energy on average, and actually increases energy consumption in 4 of the 8 households.
Jiakang Lu, Tamim I. Sookoor, Vijay Srinivasan, Brian Holben, John A. Stankovic, Eric Field, Kamin Whitehouse
SenSys5