Next Article in Journal
Toward Sustainable and Inclusive Housing: Underpinning Housing Policy as Design for Values
Next Article in Special Issue
Highly Efficient and Robust Grid Connected Photovoltaic System Based Model Predictive Control with Kalman Filtering Capability
Previous Article in Journal
A Mixed Rough Sets/Fuzzy Logic Approach for Modelling Systemic Performance Variability with FRAM
Open AccessArticle

Smart Wifi Thermostat-Enabled Thermal Comfort Control in Residences

Department of Mechanical & Aerospace Engineering, University of Dayton, Dayton, OH 45469-0238, USA
Author to whom correspondence should be addressed.
Sustainability 2020, 12(5), 1919;
Received: 31 January 2020 / Revised: 24 February 2020 / Accepted: 25 February 2020 / Published: 3 March 2020
(This article belongs to the Special Issue Intelligent Mechatronic and Renewable Energy Systems)
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. View Full-Text
Keywords: thermal comfort control; PMV; smart WiFi thermostat; mean radiant temperature; machine learning thermal comfort control; PMV; smart WiFi thermostat; mean radiant temperature; machine learning
Show Figures

Figure 1

MDPI and ACS Style

Lou, R.; Hallinan, K.P.; Huang, K.; Reissman, T. Smart Wifi Thermostat-Enabled Thermal Comfort Control in Residences. Sustainability 2020, 12, 1919.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop