PreHeat: controlling home heating using occupancy prediction

By James Scott, A. Brush, John Krumm, Brian Meyers, Mike Hazas, Steve Hodges, N. Villar. Ubiquitous Computing, 2011

384 citations recorded by the source corpus.

Subfields: Building and Construction, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/4b75d108253c3072841090914277cf92c5b4cfaa

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Building and Construction

1. M. Avci

In Computer Vision and Pattern Recognition

1. Mark W. Newman
2. Rayoung Yang
3. M. Avci

In Electrical and Electronic Engineering

1. Mark W. Newman
2. Rayoung Yang
3. M. Avci

Citations in the corpus

• Learning from a learning thermostat: lessons for intelligent systems for the home (2013, background): “, [7,8,22,23]) as well as commercial offerings [30].”
• A Survey on Concepts, Applications, and Challenges in Cyber-Physical Systems (2014, methodology): “Further, they can provide automated pre-heating or pre-cooling services based on the occupancy prediction techniques [99].”
• Demand Response-Enabled Model Predictive HVAC Load Control in Buildings using Real-Time Electricity Pricing (2013, methodology): “They indicated that their system both saved gas and reduced MissTime (the time that the house was occupied but not warm) while removing the need for users to program thermostat schedules (Scott et al., 2011).”