Smart Wifi Thermostat-Enabled Thermal Comfort Control in Residences

Robert Lou, Kevin P. Hallinan, Kefan Huang and Timothy Reissman
Additional contact information
Robert Lou: 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
Kefan Huang: Department of Mechanical & Aerospace Engineering, University of Dayton, Dayton, OH 45469-0238, USA
Timothy Reissman: Department of Mechanical & Aerospace Engineering, University of Dayton, Dayton, OH 45469-0238, USA

Sustainability, 2020, vol. 12, issue 5, 1-15

Abstract: The present research leverages prior works to automatically estimate wall and ceiling R-values using a combination of a smart WiFi thermostat, building geometry, and historical energy consumption data to improve the calculation of the mean radiant temperature (MRT), which is integral to the determination of thermal comfort in buildings. To assess the potential of this approach for realizing energy savings in any residence, machine learning predictive models of indoor temperature and humidity, based upon a nonlinear autoregressive exogenous model (NARX), were developed. The developed models were used to calculate the temperature and humidity set-points needed to achieve minimum thermal comfort at all times. The initial results showed cooling energy savings in excess of 83% and 95%, respectively, for high- and low-efficiency residences. The significance of this research is that thermal comfort control can be employed to realize significant heating, ventilation, and air conditioning (HVAC) savings using readily available data and systems.

Keywords: thermal comfort control; PMV; smart WiFi thermostat; mean radiant temperature; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (12)

Downloads: (external link)
https://www.mdpi.com/2071-1050/12/5/1919/pdf (application/pdf)
https://www.mdpi.com/2071-1050/12/5/1919/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:12:y:2020:i:5:p:1919-:d:327846

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().