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 d...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.展开更多
基金supported by the National Science Foundation(award OAC-2005572)the State of Illinois,USA.
文摘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.
