Abstract
The present research leverages prior works to automatically estimate wall and ceiling R-values using a combination of a smart WiFi thermostat, building geometry, and historical energy consumption data to improve the calculation of the mean radiant temperature (MRT), which is integral to the determination of thermal comfort in buildings. To assess the potential of this approach for realizing energy savings in any residence, machine learning predictive models of indoor temperature and humidity, based upon a nonlinear autoregressive exogenous model (NARX), were developed. The developed models were used to calculate the temperature and humidity set-points needed to achieve minimum thermal comfort at all times. The initial results showed cooling energy savings in excess of 83% and 95%, respectively, for high- and low-efficiency residences. The significance of this research is that thermal comfort control can be employed to realize significant heating, ventilation, and air conditioning (HVAC) savings using readily available data and systems.
Highlights
Climate change is primarily caused by greenhouse gas emissions, especially carbon dioxide (CO2)
Three additional general factors affect thermal comfort, including (i) other internal environmental factors (room relative humidity, air velocity, and mean radiant temperature (MRT); (ii) residential factors associated with occupant age, gender, clothing ensemble, and level of activity or metabolic rate; and (iii) occupant controls, such as the opening and closing of windows and blinds
In order to assess the variability of thermal comfort from room to room, we considered variation in terms of exterior surface connection for the rooms themselves
Summary
Climate change is primarily caused by greenhouse gas emissions, especially carbon dioxide (CO2). According to the EIA 2015 Residential Energy Consumption Survey (RECS), air conditioning and space heating account for 17% and 15% of residential electricity consumption, respectively. It is evident that minimizing heating, ventilation, and air condition (HVAC) energy consumption can reduce residential energy consumption and greenhouse gas emissions both nationally and worldwide. Three additional general factors affect thermal comfort, including (i) other internal environmental factors (room relative humidity, air velocity, and mean radiant temperature (MRT); (ii) residential factors associated with occupant age, gender, clothing ensemble, and level of activity or metabolic rate; and (iii) occupant controls, such as the opening and closing of windows and blinds. Fanger’s predicted mean vote (PMV) has generally been used to characterize thermal comfort in buildings. This model was developed by testing multiple subjects under steady-state moderated indoor environments in the 1970s. Thermal equilibrium is achieved when heat losses to the ambient environment are equal to the heat produced by the human body
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