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Understanding HVAC system runtime of U.S. homes: An energy signature analysis using smart thermostat data
Heating, ventilation, and air conditioning system runtime is a crucial metric for establishing the connection between system operation and energy performance. Similar homes in the same location can have varying runtime due to different factors. To understand such heterogeneity, this study conducted an energy signature analysis of heating and cooling system runtime for 5,014 homes across the US>using data from ecobee smart thermostats. Two approaches were compared for the energy signature analysis: (1) using daily mean outdoor temperature and (2) using the difference between the daily mean outdoor temperature and the indoor thermostat setpoint (delta T) as the independent variable. The best-fitting energy signature parameters (balance temperatures and slopes) for each house were estimated and statistically analyzed. The results revealed significant differences in balance temperatures and slopes across various climates and individual homes. Additionally, we identified the impact of housing characteristics and weather conditions on the energy signature parameters using a long absolute shrinkage and selection operator (LASSO) regression. Incorporating delta T into the energy signature model significantly enhances its ability to detect hidden impacts of various features by minimizing the influence of setpoint preferences. Moreover, our cooling slope analysis highlights the significant impact of outdoor humidity levels, underscoring the need to include latent loads in building energy models.
Understanding HVAC system runtime of U.S. homes: An energy signature analysis using smart thermostat data
Heating, ventilation, and air conditioning system runtime is a crucial metric for establishing the connection between system operation and energy performance. Similar homes in the same location can have varying runtime due to different factors. To understand such heterogeneity, this study conducted an energy signature analysis of heating and cooling system runtime for 5,014 homes across the US>using data from ecobee smart thermostats. Two approaches were compared for the energy signature analysis: (1) using daily mean outdoor temperature and (2) using the difference between the daily mean outdoor temperature and the indoor thermostat setpoint (delta T) as the independent variable. The best-fitting energy signature parameters (balance temperatures and slopes) for each house were estimated and statistically analyzed. The results revealed significant differences in balance temperatures and slopes across various climates and individual homes. Additionally, we identified the impact of housing characteristics and weather conditions on the energy signature parameters using a long absolute shrinkage and selection operator (LASSO) regression. Incorporating delta T into the energy signature model significantly enhances its ability to detect hidden impacts of various features by minimizing the influence of setpoint preferences. Moreover, our cooling slope analysis highlights the significant impact of outdoor humidity levels, underscoring the need to include latent loads in building energy models.
Understanding HVAC system runtime of U.S. homes: An energy signature analysis using smart thermostat data
Build. Simul.
Kim, You-Jeong (author) / Waegel, Alexander (author) / Hakkarainen, Max (author) / Yi, Yun Kyu (author) / Braham, William (author)
Building Simulation ; 18 ; 235-258
2025-02-01
24 pages
Article (Journal)
Electronic Resource
English
data-driven modeling , HVAC system runtime , energy signature analysis , smart thermostat dataset , ecobee DYD Engineering , Building Construction and Design , Engineering Thermodynamics, Heat and Mass Transfer , Atmospheric Protection/Air Quality Control/Air Pollution , Monitoring/Environmental Analysis
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