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Estimating smart Wi-Fi thermostat-enabled thermal comfort control savings for any residence.

Author(s) : ALHAMAYANI A. D., SUN Q., HALLINAN K. P.

Type of article: Periodical article

Summary

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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  • Original title: Estimating smart Wi-Fi thermostat-enabled thermal comfort control savings for any residence.
  • Record ID : 30029255
  • Languages: English
  • Subject: Technology
  • Source: Clean Technologies - vol. 3 - n. 4
  • Publishers: MDPI
  • Publication date: 2021/12
  • DOI: http://dx.doi.org/10.3390/cleantechnol3040044

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