ALBERT

Description: Smart WiFi thermostats, when they first reached the market, were touted as a means for achieving substantial heating and cooling energy cost savings. These savings did not materialize until additional features, such as geofencing, were added. Today, average savings from these thermostats of 10–12% in heating and 15% in cooling for a single-family residence have been reported. This research aims to demonstrate additional potential benefit of these thermostats, namely as a potential instrument for conducting virtual energy audits on residences. In this study, archived smart WiFi thermostat measured temperature data in the form of a power spectrum, corresponding historical weather and energy consumption data, building geometry characteristics, and occupancy data were integrated in order to train a machine learning model to predict attic and wall R-Values, furnace efficiency, and air conditioning seasonal energy efficiency ratio (SEER), all of which were known for all residences in this study. The developed model was validated on residences not used for model development. Validation R-squared values of 0.9408, 0.9421, 0.9536, and 0.9053 for predicting attic and wall R-values, furnace efficiency, and AC SEER, respectively, were realized. This research demonstrates promise for low-cost data-based energy auditing of residences reliant upon smart WiFi thermostats.