Document Type
Article
Publication Date
12-2021
Publication Source
Clean Technologies
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.
Inclusive pages
743-760
ISBN/ISSN
2571-8797
Document Version
Published Version
Publisher
MDPI
Volume
3
Issue
4
Peer Reviewed
yes
eCommons Citation
Alhamayani, Abdulelah D.; Sun, Qiancheng; and Hallinan, Kevin, "Estimating Smart Wi-Fi Thermostat-Enabled Thermal Comfort Control Savings for Any Residence" (2021). Mechanical and Aerospace Engineering Faculty Publications. 251.
https://ecommons.udayton.edu/mee_fac_pub/251
Comments
This open-access article is provided for download in compliance with the publisher’s policy on self-archiving. To view the version of record, use the DOI: https://doi.org/10.3390/cleantechnol3040044