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With about half of a home’s energy cost resulting from heating, ventilation, and air conditioning (HVAC), choices for the heating and cooling setpoints can have a large impact on monthly expenses. Proponents of programmable thermostats cite savings of 10-30% on energy costs, yet in practice research has shown actual savings fall short of these values due to difficulties in setup and programming of these thermostats. Current thermostats, even those programmed correctly, do no really put users in control of their spending. Instead, users specify a set of heating and cooling temperatures and don’t really know how much they will spend until they get a bill at the end of the month.
This research project was motivated around a simple concept: to change how homeowners make energy decisions by correlating the heating and cooling setpoint schedule to dollars and cents. The project spawned a company called Acomni. The thermostat monitor we make is able to learn the characteristics of each house, and with a weather forecast it can predict how much a user spends given their set points and provide recommendations to maximize comfort while balancing cost. It continuously updates this prediction, giving users up-to-date information based on changing weather patterns, set point schedules, or home characteristics.
Through this visualization users can be more informed when making their energy decisions.
This work is supported by the National Science Foundation under award IIP-1249175.
PI: Jonathan Sprinkle, Co-PI: Susan Lysecky, with Xiao Qin and Manny Teran. See the results of this research at http://www.acomni.com/
NSF