Smart Wifi Thermostat-Enabled Thermal Comfort Control in Residences

sustainability Article Smart Wifi Thermostat-Enabled Thermal Comfort Control in Residences Robert Lou *, Kevin P. Hallinan, Kefan Huang and Timothy Reissman Department of Mechanical & Aerospace Engineering, University of Dayton, Dayton, OH 45469-0238, USA; [email protected] (K.P.H.); [email protected] (K.H.); [email protected] (T.R.) * Correspondence: [email protected] Received: 31 January 2020; Accepted: 25 February 2020; Published: 3 March 2020 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 1. Introduction Climate change is primarily caused by greenhouse gas emissions, especially carbon dioxide (CO2). Power generation contributes most significantly to carbon release. In 2018, as documented by the U.S. Energy Information Agency (EIA), residential and commercial building sectors’ combined consumption represented 40% of total U.S. energy consumption. The residential sector accounts for 55% of this amount. According to the EIA 2015 Residential Energy Consumption Survey (RECS), air conditioning and space heating account for 17% and 15% of residential electricity consumption, respectively. It is evident that minimizing heating, ventilation, and air condition (HVAC) energy consumption can reduce residential energy consumption and greenhouse gas emissions both nationally and worldwide. Since 2015, there has been a marked evolution of implemented utility-related energy efficiency programs. Pilot programs managed by utility providers throughout the U.S. have documented the energy-saving potential of smart thermostats, ranging from negative to 20% savings [1]. A 2018 report on smart thermostat market characterization, prepared by a Bonneville Power Administration (BPA) research team, concluded that only “smart advanced thermostats”, which include occupancy sensing and self-learning algorithms, yield savings. One of the studies in their research effort showed an annual saving of 745–955 kWh per thermostat [2]. The well-known saving mechanism applied by smart thermostats is to maintain a high cooling temperature set-point and a low heating temperature set-point during occupied periods, with even larger increases/decreases (depending upon the season) in unoccupied periods. The former means that users must, to some extent, sacrifice their comfort to achieve energy savings [3]. However, Sustainability 2020, 12, 1919; doi:10.3390/su12051919 www.mdpi.com/journal/sustainability Sustainability 2020, 12, 1919 2 of 15 compromising indoor thermal comfort can lead to significant negative impacts on occupant health and productivity [4,5]. 2. Background The question is, therefore, how do systems simultaneously save energy and ensure thermal comfort? It is first important to understand the factors that contribute to thermal comfort. Zonal dry-bulb air temperature alone does not reflect the actual thermal sensation of occupants. In particular, the temperature measurement afforded by a thermostat only represents the indoor room temperature of the space where the thermostat is located. Three additional general factors affect thermal comfort, including (i) other internal environmental factors (room relative humidity, air velocity, and mean radiant temperature (MRT); (ii) residential factors associated with occupant age, gender, clothing ensemble, and level of activity or metabolic rate; and (iii) occupant controls, such as the opening and closing of windows and blinds. Ideally, thermostat set-points should account for most of the factors affecting thermal comfort to generate set-points that are able to establish thermal comfort at any time for the actual conditions existing in a residence. Fanger’s predicted mean vote (PMV) has generally been used to characterize thermal comfort in buildings. This model was developed by testing multiple subjects under steady-state moderated indoor environments in the 1970s. The PMV index is based upon a heat balance of the human thermoregulatory system [6]. Thermal equilibrium is achieved when heat losses to the ambient environment are equal to the heat produced by the human body. The American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE) proposed that the PMV index predicts the average vote of a large group of people on a defined thermal sensation scale [7]. This seven-point scale ranges from 3 to +3 and effectively accounts for the − perceived comfort of a majority of people. The lower and upper ends of the scale are associated with most people feeling cold and warm, respectively, as shown in Table1. Table 1. Fanger’s PMV level values and associated thermal sensation. Value Sensation +3 Hot +2 Warm +1 Slightly warm 0 Neutral 1 Slightly cool − 2 Cool − 3 Cold − The PMV index is calculated using six parameters. Four of these are environmental thermal parameters: air temperature (Ta), relative humidity (RH), mean radiant temperature (MRT), and air velocity (m/s). Two are occupant factors: clothing insulation (Clo) and activity level (MET) are related to the human metabolic rate. A comfort range, given by 0.5 < PMV < +0.5, provides reasonable − comfort for 90% of people. Of these, a smart WiFi thermostat assesses only room temperature and humidity. Proposed herein is a new approach to measure the MRT. In general, the other parameters cannot be known without additional sensors or input from the residents themselves. Thus, the following assumptions were made. (1) Activity level (MET). The MET generally ranges from 1.0 to 1.7. A conservative MET value can be estimated depending upon the physical task (e.g., 1.0 for reading or writing while sitting, 1.7 for walking about). (2) Clothing level (Clo). An indoor clothing assembly of between 0.36–0.57 and 0.61–1.01 for, respectively, summer and winter conditions is typically employed. For a minimum energy Sustainability 2020, 12, 1919 3 of 15 perspective, we assumed a clothing level reflective of a desire to save energy; thus, a Clo for, respectively, summer and winter conditions of 0.36 and 1.01 was used herein. (3) Relative air velocity/air flow (var). According to ASHRAE 55, the indoor air velocity should not exceed 0.2 m/s (39 fpm) to achieve a minimum livable condition [8]. Also, in order to reduce draft risk at any temperatures below 22.5 ◦C (72.5·◦F), airspeed due to the HVAC system must be 0.15 m/s (30 fpm) or below [9]. Therefore, the relative air velocity was assumed to be 0.1 m/s (19.7 fpm). A number of researchers have investigated various active thermal comfort control approaches in residences based upon the PMV. In these studies, control methodologies have included fuzzy logic (FLC) [10] and neural network (NN) [11] based predictive controllers. Prior researchers have succeeded in simultaneously maintaining thermal comfort and reducing energy consumption. In [10], the authors utilized a complicated hierarchical FLC with a 3D fuzzy set to represent thermal comfort based upon the PMV indicator, indoor illumination, and CO2 level. Its membership function constraints were tuned by a genetic algorithm (GA). The authors noted a roughly 8% energy increase in order to satisfy more occupants. In [11], a discrete model-based predictive controller was developed. A cost function to optimize the controller by minimizing energy consumption and maintaining thermal comfort was developed. Energy savings in relation to a standard constant temperature setpoint control l, ranging from 41% - 77%, were realized. Table2 summarizes the research conducted in this arena. Included in this table are descriptions of the MRT determination, the thermal comfort assessment, the assumed factor values in calculating the PMV, the control techniques employed, the energy savings derived, and the sensors and other hardware employed. The latter is particularly important. The requirement of sensors not available in systems already present in residences poses a substantial barrier to market penetration. Sustainability 2020, 12, 1919 4 of 15 Table 2. Summary of calculation of MRT for use in thermal comfort calculations. Author Thermal Comfort Sensors/Other MRT Assumed Factors Control Technique Energy Savings (Year) Assessment Hardware Suggested consideration of Clothing = 0.6–0.8 Clo NN based on PMV index to Torres et al. (2008) thermal radiation to/from Standard Fanger’s (summer) Not directly control setpoint through a Not mentioned [12] walls; however, no clear PMV formulation Airflow = 0.1 m/s measured PI controller method described MET = 1–1.7 Models developed to Clothing

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