New from Microsoft Research: Controlling Home Heating Using Occupancy Prediction

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New from Microsoft Research: Controlling Home Heating Using Occupancy Prediction

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Our friends over in Microsoft Research recently showcased a project, called PreHeat, which is designed to save energy by automatically controlling a home’s thermostat based on predictions of when the occupants will be home. In the U.S. and U.K., home heating uses more energy than any other residential energy expenditure. In the U.S., fewer than half the houses have programmable thermostats, and many of those are used as a manual on/off switches for the furnace.

PreHeat uses occupancy sensors to log when a space (room or whole house) is occupied. The system then runs a new pattern-matching algorithm to predict when the space will be occupied so it can turn on the heat in anticipation of the occupant’s arrival. Their experiments found that it often takes about 90 minutes for a house to heat up to the target temperature in the winter.


Part of the project involved custom-built thermostats and occupancy sensors, created from the .NET Gadgeteer hardware at Microsoft Research in Cambridge, UK. The Gadgeteer components communicated with a server PC in each home, which logged experimental data and ran the prediction algorithm.

The researchers deployed PreHeat in five different houses over the winter for testing. The two U.K. houses featured separate heating controls for each room, while the three U.S. houses used whole-house, forced air furnaces. The experiment compared predictive PreHeat control with a carefully programmed thermostat schedule, alternating days between the two conditions to factor out the effect of weather.

In the U.S. houses, PreHeat used about the same amount of heating gas as the programmable thermostat, but there was a marked improvement in the comfort of the home’s occupants, measured by the amount of time the house was too cold while the occupants were there. In the U.K. houses, PreHeat simultaneously increased comfort and saved a significant amount of gas.

The research is continuing with the aim of a more sophisticated anticipatory heating schedule and a UI for adjusting the behavior of the predictive system.

You can learn more about PreHeat over on the MSR project web page:

There’s also a paper describing the project which is led by researchers: James Scott, A.J. Bernheim Brush, John Krumm, Brian Meyers, Michael Hazas, Steve Hodges, and Nicholas Villar

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