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Inicio  /  Clean Technologies  /  Vol: 3 Par: 4 (2021)  /  Artículo
ARTÍCULO
TITULO

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

Abdulelah D. Alhamayani    
Qiancheng Sun and Kevin P. Hallinan    

Resumen

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.

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