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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Abdulelah D. Alhamayani & Qiancheng Sun & Kevin P. Hallinan, 2021. "Estimating Smart Wi-Fi Thermostat-Enabled Thermal Comfort Control Savings for Any Residence," Clean Technol., MDPI, vol. 3(4), pages 1-18, October.
    • Handle: RePEc:gam:jcltec:v:3:y:2021:i:4:p:44-760:d:654041
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    References listed on IDEAS

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