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Estimation des économies réalisées par les thermostats Wi-Fi intelligents en matière de contrôle du confort thermique pour tout type de résidence.

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

Auteurs : ALHAMAYANI A. D., SUN Q., HALLINAN K. P.

Type d'article : Article de périodique

Résumé

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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Pages : 18

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Détails

  • Titre original : Estimating smart Wi-Fi thermostat-enabled thermal comfort control savings for any residence.
  • Identifiant de la fiche : 30029255
  • Langues : Anglais
  • Sujet : Technologie
  • Source : Clean Technologies - vol. 3 - n. 4
  • Éditeurs : MDPI
  • Date d'édition : 12/2021
  • DOI : http://dx.doi.org/10.3390/cleantechnol3040044

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