Estimating Smart Wi-Fi Thermostat-Enabled Thermal Comfort Control Savings for Any Residence

Abdulelah D. Alhamayani, Qiancheng Sun and Kevin P. Hallinan
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Abdulelah D. Alhamayani: Department of Mechanical & Aerospace Engineering, University of Dayton, Dayton, OH 45469-0238, USA
Qiancheng Sun: Department of Mechanical & Aerospace Engineering, University of Dayton, Dayton, OH 45469-0238, USA
Kevin P. Hallinan: Department of Mechanical & Aerospace Engineering, University of Dayton, Dayton, OH 45469-0238, USA

Clean Technol., 2021, vol. 3, issue 4, 1-18

Abstract: Nowadays, most indoor cooling control strategies are based solely on the dry-bulb temperature, which is not close to a guarantee of thermal comfort of occupants. Prior research has shown cooling energy savings from use of a thermal comfort control methodology ranging from 10 to 85%. The present research advances prior research to enable thermal comfort control in residential buildings using a smart Wi-Fi thermostat. “Fanger’s Predicted Mean Vote model” is used to define thermal comfort. A machine learning model leveraging historical smart Wi-Fi thermostat data and outdoor temperature is trained to predict indoor temperature. A Long Short-Term-Memory neural network algorithm is employed for this purpose. The model considers solar heat input estimations to a residence as input features. The results show that this approach yields a substantially improved ability to accurately model and predict indoor temperature. Secondly, it enables a more accurate estimation of potential savings from thermal comfort control. Cooling energy savings ranging from 33 to 47% are estimated based upon real data for variable energy effectiveness and solar exposed residences.

Keywords: thermal comfort control; solar heat gain; PMV; energy saving; smart Wi-Fi thermostat (search for similar items in EconPapers)
JEL-codes: Q2 Q3 Q4 Q5 (search for similar items in EconPapers)
Date: 2021
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