Abstract
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.
Highlights
In 2018, according to the U.S Energy Information Administration (EIA), residential buildings accounted for approximately 21% of total electricity consumption as well as 16%
While residential energy reduction offers the most cost-effective potential among all U.S buildings, the vast majority of this savings potential comes from low-income residences [4,5,6]
Relevant research pertaining to the standard calculation approaches is presented for: building energy models with sufficient granularity to permit estimates of savings from residential energy upgrades, inverse modeling approaches with sufficient granularity to identify residences in need of upgrades and quantity the resulting savings based on energy data pre- and post-upgrade, and the state-of-the art associated with virtual energy audits
Summary
In 2018, according to the U.S Energy Information Administration (EIA), residential buildings accounted for approximately 21% of total electricity consumption as well as 16%of total natural gas consumption in the U.S [1,2]. The rebated measures are generally those with the statistically best savings relative to investment among the entire residential population. What this has meant is that all rate payers have effectively subsidized the investments of wealthier residents. Energy modeling software (e.g., eQuest, EnergyPlus, IES, and Energy-10) has been used extensively to simulate and predict building energy consumption. These have required extensive detail about the geometric and energy characteristics of a building, as well as occupancy and control schedules. These tools overpredict energy consumption [16]
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