A Smart WIFI Thermostat Data-Based Neural Network Model for Controlling Thermal Comfort in Residences Through Estimates of Mean Radiant Temperature

Date of Award


Degree Name

Ph.D. in Mechanical and Aerospace Engineering


Department of Mechanical and Aerospace Engineering


Timothy Reissman


Indoor thermal comfort in residential buildings is usually achieved by tenants manually adjusting fixed temperature set-points; this is known as a 'static' method. Prior research has explored automated control of thermal comfort based on the concept of a Predicted Mean Vote (PMV) index, which has been developed to provide a model of perceived human comfort. However, one of the dominant contributions to this index, the Mean Radiant Temperature (MRT), effectively the mean radiant temperature of the surrounding interior surfaces, has either been: 1) inaccurately assumed to be the same as indoor air temperature; and/or 2) costly to implement due to the need for numerous additional sensors. Research is posed to leverage prior work in automatically estimating the R-values of walls and ceilings using a combination of smart WiFi thermostat, building geometry, and historical energy consumption [51] to estimate the MRT with accuracy and thus provide a means to control for comfort, rather than temperature alone. In order to assess the energy saving potential of comfort control for any residence, a machine learning model of the indoor temperature based upon a NARX Neural Network is employed. This model leverages historical thermostat and weather data to develop a means to dynamically predict the interior temperature. With a developed model, it is possible to simulate different temperature set-points on indoor temperature, and thus identify the optimal set-point temperature at all times needed to maintain a reasonable comfort condition. Application of this ideal temperature set-point for minimum human comfort to historical weather data and indoor weather conditions can yield an estimate for minimum cooling energy. The initial results showed cooling energy savings in excess of 83% and 95%, respectively, for high- and low-efficiency residences. Based on this research, it is proposed that the approach to estimate MRT can be used to calculate a more accurate PMV value and a better representation of human comfort, without anything more than a smart WiFi thermostat with readily available data. Thus, a control strategy based on this paradigm can both achieve thermal comfort in residential buildings and less energy consumption. In addition, a Model Predictive Controller (MPC) is developed to realize more realistic and sensible control. Compressor protection is also considered in the development of the controller.


Mechanical Engineering, Energy, thermal comfort control, PMV, mean radiant temperature, machine learning, neural network, indoor temperature prediction, model predictive control, smart WiFi thermostat

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