TrojanSense, a participatory sensing framework for occupant-aware management of thermal comfort in campus buildings

Journal Pre-proof TrojanSense, a Participatory Sensing Framework for Occupant-Aware Management of Thermal Comfort in Campus Buildings Kyle Konis, Simon Blessenohl, Naman Kedia, Vishal Rahane PII:

S0360-1323(19)30800-5

DOI:

https://doi.org/10.1016/j.buildenv.2019.106588

Reference:

BAE 106588

To appear in:

Building and Environment

Received Date: 26 September 2019 Revised Date:

16 November 2019

Accepted Date: 5 December 2019

Please cite this article as: Konis K, Blessenohl S, Kedia N, Rahane V, TrojanSense, a Participatory Sensing Framework for Occupant-Aware Management of Thermal Comfort in Campus Buildings, Building and Environment, https://doi.org/10.1016/j.buildenv.2019.106588. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.

TrojanSense, a Participatory Sensing Framework for Occupant-Aware Management of Thermal Comfort in Campus Buildings Author Names: Kyle Konis, Ph.D, Simon Blessenohl, Naman Kedia, Vishal Rahane Cor. Author Address: Kyle Konis, Ph.D Watt Hall, Room 204. University of Southern California Los Angeles, CA, 90089-0291 USA Cor. Author Contact: [email protected] Phone: 1-206-303-9786 Declaration of Interest: Kyle Konis is an employee of the University of Southern California (USC) (assistant professor). Simon Blessenohl and Naman Kedia are current students at USC, and Vishal Rahane was a student at the time the research being reported was conducted. Naman Kedia received funding from a USC Undergraduate Research Associates Program (URAP) award and Naman Kedia and Vishal Rahane received funding from Prof. Konis’ faculty research development account. Equipment used in the research (Estimote proximity beacons) were purchased using funds from an award received by Simon Blessenohl from the USC Green Engagement Fund. Abstract This paper describes the development of TrojanSense, a participatory sensing framework developed to collect, analyze and report user assessments of thermal preference at the campus scale with room-level spatial resolution. TrojanSense was developed with the goal of supporting initiatives to improve campus community engagement on issues of occupant thermal comfort, energy efficiency, and equity in the environmental control of university buildings. TrojanSense is an open-loop system, where outcomes pairing thermal preference and concurrent indoor temperature measurements are analyzed and reported with room-level resolution on a Campus Thermal Preference Map to support building operators and to inform campus policies related to thermal comfort and energy efficiency. The TrojanSense framework integrates wireless Bluetooth Low Energy (BLE) proximity beacons embedded in campus buildings to sense temperature and to automatically solicit occupant feedback using proximity-based prompts. Data from an exploratory pilot study are analyzed to examine the applicability of the approach for developing predictive models of thermal preference and for placing existing space conditioning assumptions in context with occupant feedback. Results demonstrate the potential for TrojanSense to identify overcooling using objective and subjective measures, increase the accuracy of predictions of thermal preference, and provide greater insight into the impact of outdoor weather on thermal preference to inform future climate-responsive control strategies.

1. Introduction 1.1 Thermal Management in Institutional Buildings In contrast with academic buildings in the U.S. constructed prior to the 1950s, which often had relatively narrow floorplates (e.g. 20m) and operable windows for natural ventilation and passive cooling, modern academic buildings are often characterized by deep floorplates and a sealed building envelope, where Heating, Ventilation, and Air Conditioning (HVAC) energy is consumed throughout the year to maintain steady-state and homogeneous indoor temperatures within the thermostat setpoint range (deadband) considered necessary to maintain occupant thermal comfort. HVAC systems currently represent the largest energy end use in many university buildings in the U.S., and account for over 40% of total building energy use on average for institutional buildings and 60% for laboratory buildings [1]. Although increasingly sophisticated systems are being deployed at the campus scale to collect, analyze, and use information on building energy consumption to address campus sustainability goals [2-5], effective and scalable tools for putting energy data in context with occupant subjective assessments of comfort that go beyond the scale of individual rooms or buildings are extremely limited [6]. 1.2 PMV Model and Adaptive Thermal Comfort The air temperature setpoint range in buildings continues to be based on application of static comfort proxies such as the Predicted Mean Vote (PMV) model [7] adopted by ANSI/ASHRAE Standard 55-2013 [8], or, in many cases, the judgement of a building operator responding to an ad-hoc complaint rather than the conditions preferred by occupants in a given thermal zone during a particular time of year. In contemporary buildings, this lack of subjective feedback often results in overcooling or overheating of spaces beyond the comfort requirements of occupants (i.e. over-conditioning), and can result in a considerable waste of energy [9,10]. The Predicted Mean Vote (PMV) model is the most widely used thermal comfort model, and was developed in controlled laboratory setting (thermal comfort chamber) under steady state conditions specifically for the operation of centralized, HVAC-controlled buildings. In contrast with the PMV approach, which uses a heat balance model of the human body to predict thermal sensation, adaptive thermal comfort is based on the theory that preferred indoor temperatures depend not only on indoor environmental factors (temperature, thermal radiation, humidity and air speed) and personal factors (clothing and activity level) but also on outdoor weather. Adaptive theory is based on the recognition that humans exhibit the ability to adapt psychologically to their environment and thus subjective assessments of comfort are dependent on context. This theory is supported by research on field data collected in naturally ventilated and “mixed-mode” buildings, which shows that occupants are accepting of a broader range of air temperatures compared with sealed, air-conditioned buildings [11]. The Adaptive Comfort Standard (ACS) [12], informed by field research and incorporated into ASHRAE-55, provides a compliance pathway for naturally ventilated buildings (e.g. buildings with operable windows) which are often more energy efficient than sealed and mechanically controlled alternatives. However, adaptive comfort methods are rarely applied to manage thermal conditions in buildings without operable windows, which constitute the majority of university buildings in the present

study. Research on thermal adaptation in the built environment [13] has also shown that thermal perception in “real world” settings is influenced by additional factors such as past thermal history, non-thermal factors (such as the level of perceived control over the thermal environment), and thermal expectations. A recent analysis of the accuracy of the PMV model in predicting thermal preference (i.e. “prefer cooler”; “no change”; “prefer warmer”) in real buildings using the ASHRAE Global Thermal Comfort Database II [14], reported that the accuracy of the PMV model in predicting the observed thermal sensation of occupants was only 34%, and that prediction accuracy varied widely among various building types, ventilation strategies, and climate groups [15]. These findings indicate that there is significant potential to improve operational assumptions for human thermal comfort in buildings. Additional research is needed to examine the applicability of adaptive theory to explain the relationship, if any, between subjective assessments of thermal preference in conventional institutional buildings and outdoor environmental factors. 1.3 Setpoint Adjustments Adjustment of the thermostat setpoint range (deadband) is known to be one of the most practical and cost-effective strategies for reducing space heating and cooling energy consumption [16-18]. Even a slight extension to the temperature setpoint range (i.e. a larger deadband) can have a significant impact on building HVAC energy because no heating or cooling is required when a space is operating within the deadband. For example, a setpoint adjustment extending the cooling setpoint (e.g. from 23ºC to 25ºC) reduces cooling loads through the reduction in cooling hours and by decreasing the difference between the set-point and the outdoor temperature. For buildings with Variable Air Volume (VAV) systems, operating within the deadband also allows the terminal unit airflow volume rate to be reduced to the design minimum, leading to a reduction in the energy consumed by fans [18]. A simulation-based parametric study aimed at systematically quantifying the HVAC savings potential of extending air temperature setpoints reported that an increase in the cooling setpoint of 22.2°C (72°F) to 25°C (77°F), resulted in an average of 29% of cooling energy savings and 27% total HVAC energy savings across a range of U.S. climates without reducing satisfaction levels [18]. Institutional settings are unique in that a central office is typically responsible for establishing and overseeing a setpoint policy across a portfolio of buildings, which are likely to vary in regard to size, building type, age (i.e. year of construction), user population, type and age of HVAC system, and the level of sophistication of the Building Management System (BMS), if one exists. Temperatures in institutional buildings are typically regulated remotely, and problems related to thermal discomfort are typically reported by making a phone call to the central office. Significant comfort improvements and energy savings may be possible in thermal zones where overcooling or overheating are reported, or where occupants are accepting (or even preferential to) an extended temperature setpoint range. Participatory sensing techniques have significant potential to identify the acceptable setpoint range in conditioned spaces, which is likely to vary across different types of spaces, buildings and groups of occupants as well as in response to changes in outdoor weather.

1.4 Participatory Sensing In contrast to conventional Post Occupancy Evaluation (POE) survey methods [19], which typically apply standard scales [20] to quantify the average response of a large population to a broad range of Indoor Environmental Quality (IEQ) factors, participatory sensing techniques are generally characterized by a focus on a single IEQ factor (i.e. thermal comfort) and methods involving repeated-measures collection of subjective data to inform environmental controls applications for a specific group of users. Prior research studies have applied participatory sensing with the objective of improving thermal comfort and/or reducing HVAC energy consumption relative to conventional operational practices [21-25] and to generate personalized thermal comfort models [26, 27]. Due in part to the widespread diffusion of mobile devices in the past decade, the most common approach is the development of a smartphone application (app) to collect occupant subjective feedback with the objective of informing closed-loop control of HVAC systems. For example, Erickson and Cerpa [22] developed a smartphone app to collect subjective thermal comfort data in one floor of a university research building and control a BACnet-enabled HVAC system. They reported HVAC energy savings of 10.1% over a 5month test interval with a satisfaction rate of 100% among 39 participants. Ghahramani et. al. [24] developed a participatory approach that controlled HVAC operations using zone temperature setpoints determined by taking additional factors beyond occupant comfort into consideration, including energy, indoor air quality, and system performance constraints and reported a 12.08% reduction in VAV terminal airflow rate (as a proxy for energy savings). Building Robotics Inc. developed a smartphone app (titled Comfy) to provide occupants with real-time control over their local VAV unit. A self-evaluation by the Comfy developers involving 16 office environments of varying size across the US and in India demonstrated a wide range in thermal preferences among occupants for a given indoor temperature, indicating the potential to improve comfort and energy performance by adjusting temperature setpoint ranges in different spaces to the preferences of real occupants. However, the study did not report on measured HVAC energy savings [25]. Digital survey interfaces have also been incorporated into desktop computer applications to place building operations in context with occupant feedback [28]. Physical survey interfaces have also been developed. Examples include desktop polling station devices to collect repeated-measures survey feedback from occupants in regularly occupied spaces [29] and polling stations and kiosks for use in public spaces [30]. Researchers have also explored tracking occupant behavior and use of physical thermal control devices (e.g. fans, heated/cooled chairs) as a means of collecting occupant feedback on thermal comfort and preference [27]. The primary benefit of physical survey interfaces over digital (i.e. screen-based) interfaces is that participants can interact with a physical device without disruption to screenbased work activities or the need to use their personal mobile device. Tracking occupant’s regular use of physical thermal control devices has the potential to address the risk that repeated surveys may lead to fatigue and decay in participation over time. The primary limitation of physical survey interfaces in research applications is the inability to scale up participation to large study populations (e.g. campus-scale applications). Tracking occupant use of thermal control devices has a similar scale limitation due to the need to distribute purpose-built physical devices to the study population. Further, methods which rely on the provision of desktop fans, heating/cooled chairs are limited to space types with regular occupancies and sedentary

populations (e.g. office environments). Finally, continuous tracking of individual behavior may be viewed by some building occupants as overly invasive or raise concerns about privacy. Finally, the incorporation of unrestricted and voluntary subjective feedback enabled by participatory sensing raises challenges related to how to define and evaluate fairness in regard to controls outcomes [31] and how to identify and address anomalous voting patterns [32]. These are particularly significant concerns when subjective feedback is proposed as the sole means for automated closed-loop building controls applications, where one individual’s voting behavior may have unfair influence over the preferences of other building occupants, or where anomalous voting patterns may limit reliability or confidence in data-driven comfort models. An anomaly detection framework developed to differentiate “outliers” from inter-individual variabilities in thermal response represents one promising approach to address these challenges [32]. However, any approach which removes “outlier” data is at risk of removing valid data and oversimplifying the conditions being studied. Shin et al. [31] developed an algorithm to evaluate “fairness” specifically for participatory sensing / thermal control applications and demonstrate that there is often a trade-off between maximizing fairness and comfort in the scenarios they explored. It is important to emphasize that issues of fairness and biased decision-making are not unique to participatory sensing. The same general issues are present in conventional approaches to thermal control in buildings where decision-making is based on outcomes of controlled laboratory research [33], or in real building applications where the setpoints are re-configured (via work order or complaint) to respond to the unique preferences of a single building occupant. 1.5 Incorporation of Outdoor Weather Variables in Controls Studies have also explored the incorporation of outdoor weather variables in controls to address the risk of occupant-driven HVAC controls converging on a narrow temperature setpoint range, and/or a large discrepancy between indoor and outdoor temperatures that would limit energy efficiency objectives. In a 40-week study involving 61 employees in 3 buildings on a university campus, Winkler et al. [34] found that HVAC systems autonomously controlled by data from a comfort voting application resulted in increased thermal satisfaction (ranging from 33.9 – 93.3%) and up to a 18.99% reduction in energy consumption compared to a system without voting. Energy savings up to 37% were found when a “drift” control strategy was applied, where heating/cooling setpoints were gradually extended from the setpoints determined based on voting towards a limit specified by building management (i.e. 18.9 °C (66 °F), 24.4 °C (76 °F)). Purdon et. al. [35] applied a similar “drift” control strategy in a 3-week study conducted in 12 offices in a university building in Australia, where temperature set points slowly drifted toward the outdoor temperature in the absence of occupant feedback indicating discomfort. The authors reported up to 60% energy reduction. The university campus where the present study is situated is located in a relatively mild climate, which makes it particularly well-suited to serve as a test-bed due to the frequent exposure of students, staff and faculty to the outdoors year round. 1.6 Scalability Only one study of has been purposefully conceived with the goal of scaling participatory sensing and closed-loop HVAC control across a large university campus [36]. The project, titled TherMOOstat, is a campus-wide participatory sensing program at the University of California,

Davis. After 23 months of operation, Sanguinetti et. al. [36] reported more than 10,000 feedback submissions and 4000 unique users. Users are required to self-report room/building location for each response, and subjective assessments of thermal comfort can be viewed in aggregate at the building level on a public campus TherMOOmap [37]. Analysis of subjective assessments with average outdoor air temperature revealed a greater proportion of ‘Cold’/‘Chilly’ votes occurred during warmer months and a greater proportion of ‘Hot’/‘Warm’ votes occurred during cooler months, indicating over-conditioning of campus spaces (see [36] Fig. 11). However, the researchers reported difficulty interacting with BAS control sequences due to the lack of a modular and universal controller for all buildings as well as difficulty with the time-intensive task of mapping BAS zones and sensors to rooms to enable room level analyses. Therefore, the application of automated closed-loop control incorporating user feedback was limited to a small number of buildings on campus. 1.7 Limitations Prior studies of participatory sensing have largely focused on improving control algorithms for conventional forced-air HVAC applications in sealed and mechanically controlled buildings. With the exception of the TherMOOstat project, these studies have been limited in scale to testbed environments ranging from a single office to a single building and primarily focused on sedentary office space types. Participatory sensing frameworks that can be scaled to the full campus and across a broad range of space types and programmatic uses are needed to capture the full energy savings potential achievable from the identification and minimization of overconditioning in buildings. Participatory sensing can also help to better ensure comfort outcomes for a large proportion of the campus population (i.e. students), who may be less likely to report thermal discomfort using existing reporting mechanisms. It is important to note that any approach to data collection which relies on building users to voluntarily submit feedback on thermal preference is susceptible to self-selection bias, where those experiencing thermal discomfort may be more likely to participate than those who are comfortable. However, it should be emphasized that conventional approaches to resolving thermal comfort complaints (e.g. phone calls to central building management office) are susceptible to the same risk of bias, and are likely to involve far fewer participants. Therefore, the creation of a framework that encourages more widespread and diverse participation on a university campus may help to reduce, but will not eliminate self-selection bias. The potential to acquire campus-wide feedback is also critical to build a body of evidence which can be used to validate and improve existing campus-wide thermal management policies, assess the impact of demand-side management actions that impact indoor environmental conditions, and to better understand how thermal preferences may vary across different building types, ages, environmental control systems, and user groups. Thus, the data generated through participatory sensing serves not as a replacement, but as a supplement to other forms of data available to building operators. The following section describes the design and development of TrojanSense. TrojanSense is a participatory sensing framework that builds on previous studies, particularly the “blueprint” demonstrated by the TherMOOstat group [6,36], by integrating wireless Bluetooth Low Energy (BLE) proximity beacons embedded in campus buildings to automatically solicit user feedback

using proximity-based prompts as an alternative to manual self-reporting of room location. Integration of proximity sensing provides the additional capability to “sub-zone” large spaces (i.e. to define multiple regions of analysis within a large open-plan floor) or where the room designation is ambiguous (i.e. the upper and lower levels of a double-height gallery space). TrojanSense is an open-loop system where outcomes pairing thermal preference and concurrent indoor temperature measurements are analyzed and reported on a Campus Thermal Preference Map (Fig. 3) developed to support building operators and to inform campus policies related to indoor thermal management for energy and comfort goals. The Campus Thermal Preference Map goes beyond the TherMOOstat mapping approach by enabling room-level reporting and includes display of objective indoor air temperature data in addition to subjective data. Integration of proximity beacons provides the capability to automatically pair subjective response data with air temperature measurements from onboard sensors, which avoids the zone-by-zone challenges in identifying and networking with existing room thermostats. The TrojanSense mobile app additionally collects basic demographic information of age group, gender and user type (during a 1-time setup process after installation) to gain greater insight into the factors that may influence thermal preferences across a university with a diverse population of staff, students and faculty. Radiant effects (i.e. variations between surface temperatures and ambient air temperature), participant clothing level, and activity level are currently not measured. The latter two factors could be assessed by incorporating additional survey questions into the mobile app. However, this was not implemented for the present study to avoid increased burden on the user in terms of survey time. TrojanSense is at an early stage of development and, as a student-and-faculty-initiated endeavor, it has not yet been adopted by campus facility management as a campus-wide participatory sensing system. A central goal of the pilot study presented in this paper is to validate TrojanSense and demonstrate its potential to provide greater insight into the impacts of current control strategies and comfort assumptions. Data collected during the pilot study represents subjective feedback under normal operating procedures. No interventions were made to building controls during the pilot study, and consequently reporting on measured HVAC energy reduction is outside the scope of the pilot study. Controls interventions and HVAC energy monitoring are considered as subsequent goals which can be addressed in future studies with greater integration and coordination with facilities management personnel. With a staff of over 20,000, more than 4,000 full time faculty, and 44,000 students, the long-term goal of the TrojanSense initiative is to leverage the diversity of people and buildings at the University of Southern California (USC) to better understand the comfort and energy impacts of the existing environmental control paradigm and build a body of evidence to support innovation in domains of building design, facilities operations and human behavior.

2. Method 2.1 The TrojanSense Open-Loop Feedback System Fig. 1 presents the framework of the TrojanSense open-loop feedback system. Users report subjective feedback using either of two interfaces, 1) a smartphone app that must be installed on

the user’s personal mobile device, or, 2) a web-based interface directly accessible from any device with a web browser. Users were recruited via a recruitment script distributed by the research team to student sustainability groups, the USC Office of Sustainability and the USC School of Architecture (where one of the authors is a faculty member). Recruitment was additionally supported by news items published by the USC Center for Cyber-Physical Systems and the Internet of Things and the USC student-run newspaper (the Daily Trojan). Two members of the research team also used the app during the pilot study. During installation of the smartphone app, the user is prompted to report basic demographic factors of Age Group, Gender, and User Type (i.e., student/staff/faculty designation). However, the user was not required to report these factors to complete the installation. A randomly generated unique identifier (app ID) was generated to preserve the anonymity of each user. Users of the web-based interface were not asked to report demographic information. Users report subjective assessments of thermal preference using a “Virtual Thermostat” UI (Fig. 2), which are stored on a secure server where data are analyzed and presented in the form of an interactive Campus Thermal Preference Map reporting summary outcomes for each space (Fig. 3). The design and intended use of the Virtual Thermostat UI and Campus Thermal Preference Map are discussed in greater detail in the following sections.

Fig. 1. Framework of the TrojanSense open-loop feedback system.

2.2. Proximity Beacons TrojanSense incorporates BLE proximity beacons (brand: Estimote proximity beacons) to automate the following processes: 1) triggering of subjective surveys based on user room location, 2) acquisition of room air temperature measurements, and 3) pairing of response data with room location. Automation of these processes is important for enabling campus-wide scalability of participatory sensing because they eliminate the burden placed on the user to manually input location prior to each response and the challenges of obtaining air temperature measurements from existing room thermostats (if available) as described in Section 1.6. Bluetooth Low Energy beacons are a form of wireless communication technology primarily developed and promoted for use in proximity-based marketing applications. A proximity beacon broadcasts packets of data at regular intervals of time, enabling other BLE compatible devices in range of a beacon to determine information such as distance (from beacon) as well as other data acquired by onboard sensors such as air temperature, ambient light level, and motion. A total of 23 proximity beacons were used in the pilot study. The objective of beacon placement at the campus level was to sample a broad range of space types that are publicly accessible and where occupants do not have direct control over a room thermostat. A secondary objective was to utilize the proximity feature of beacons to delineate smaller zones within large open-office / studio spaces. Therefore, beacons were placed in a range of space types including library, large lecture hall, public student study/gathering space, conventional classroom and large open-plan studio. Beacons were placed at the room level on a case-by-case basis in a location where the beacon was not visible to occupants, and generally within 1m – 3m from the floor. The primary objective of placement at the room level was to avoid loss of the device due to theft or removal by curious students or staff. Because the proximity beacons used in the study were not designed for precise indoor thermal measurements, the onboard temperature sensors are significantly less accurate than research instruments commonly used for indoor thermal monitoring. The temperature sensors embedded in the beacons used in the study have a reported measurement error up to ±2°C [38]. For comparison, a commonly used datalogging device (Onset HOBO HOBO Temperature/Relative Humidity Data Logger) has a reported accuracy of ± 0.35°C [39]. Therefore, results reported in Section 4 should be interpreted in context with this limitation in sensor accuracy. Using the TrojanSense mobile app, when a user walks within a specified distance of a beacon, a notification prompting the user to report their thermal preference would appear on the user’s screen along with the room or zone name to indicate that the user’s location was already entered (Fig. 2). This approach required a one-time calibration of “trigger distance” to ensure that the survey was only triggered when the user was within the desired zone. It is important to note that the user’s room location is only submitted to the server by the app if the user voluntarily chooses to complete and submit the subjective thermal preference survey. Integration of proximity beacons provided the additional capability to define “sub-zones” within a large open-plan studio floor as well as the sub-zoning of a multi-level space.

2.3. Mobile App User Interface Fig. 2 presents the sequence of screens developed for the TrojanSense mobile app User Interface (UI). The UI was developed using an iterative process, taking into account feedback from students who preferred an adjustable thermostat interface to report thermal preference over more commonly used semantic scales for the assessment of thermal comfort [20]. Prospective users of TrojanSense were informed that the Virtual Thermostat was a subjective survey instrument and would not lead to real-time adjustments in room temperature, but instead would be communicated to building operators as feedback to inform future setpoint adjustments. The first screen is a welcome screen which indicates that the user is within the proximity of a TrojanSense beacon. The user is then prompted to indicate their thermal preference by adjusting the Virtual Thermostat UI. Units (Imperial / SI) could be specified by the user during installation (and changed in settings). The user registers their thermal preference by adjusting the Virtual Thermostat UI in increments of +/-0.5 °C (or 1.0 °F) by clicking on the up or down arrows. Indicating a preference for an increase in temperature causes the color of the Virtual Thermostat to change to a warmer hue, and a preference for a decrease in temperature changes the color to a cooler hue. Once the user submits subjective feedback using the Virtual Thermostat, the user is presented with a confirmation screen and provided with an option to input additional open-ended feedback using a text field. This additional feedback channel was added to provide users with an option to report additional feedback about a given location (or usability of the app). This feature was also considered valuable as a means of investigating outliers in the data and to gain greater insight into the drivers of more extreme responses. The web-portal version of the UI (Fig. 1) is identical but requires the user to self-report room location. User feedback data is paired with concurrent readings of air temperature obtained from the proximity beacon and submitted to a secure server where it is paired with outdoor weather data collected from a NOAA weather station located on campus (USAF-WBAN_ID: 722874 93134, station name: Downtown L.A./USC Campus, http://www.ncdc.noaa.gov/data-access/quick-links).

Fig. 2. TrojanSense mobile app user interface screens (shown sequentially from left to right).

2.4. Campus Thermal Preference Map The Campus Thermal Preference Map was developed to provide building energy management personnel with the capability to rapidly identify and view thermal preference outcomes with room-level granularity. Each space with a beacon is marked on a 2-dimensional map of the campus using a color-coded icon where the color reflects the mean user response for each space. Clicking on a marker, as shown in Fig. 3, displays a pop-up window over the map summarizing the distribution of thermal preference votes and concurrent air temperature measurements recorded by the beacon over a specified time interval (in this example, Fall Semester 2018). The color code used is consistent with the colors used for the Virtual Thermostat app, thus warmer hues indicate a preference for an increase in temperature, and cooler hues indicate a preference for a decrease.

Fig. 3. Example screenshot of Campus Comfort Dashboard showing pop-up summary for the space “Watt Hall Upper Rosendin.” 2.5 Data Sets Data were used from an exploratory pilot test of TrojanSense conducted between March 2018 and April 2019 to explore the applicability of TrojanSense for developing data-driven predictive models and for placing existing space conditioning assumptions in context with occupant feedback. The study was approved University of Southern California University Park Institutional Review Board (record number: UP-18-00188) and the majority of responses (62%) were recorded during the Fall Semester of 2018. The test involved the deployment of 23 proximity beacons to spaces on the university campus and deployment of the web-portal UI for

reporting by campus users in any space on campus. The pilot test resulted in five datasets, namely: 1) Basic demographic data reported by participants during app installation (Table 1). 2) Subjective response data (Virtual Thermostat Adjustment) collected from the app and paired with beacon data (beacon ID and air temperature) and timestamp. (N = 52 participants, N = 308 total observations) 3) NOAA weather data from a weather station located on the university campus. 4) Subjective response data (Virtual Thermostat Adjustment) paired with self-reported room location collected from the web-portal UI (N=841 responses, N=77 campus buildings). 5) Open ended text feedback (collected from both the mobile app and the web-portal UIs). Table 1 presents a summary of responses reported using the mobile app based on demographics. It is important to note that the data are unbalanced in regard to response rates across Gender, User-Type and Age, and reflect an overall low level of participation (N=308 responses total among 52 participants) given the length of the study. Therefore, the authors consider the proximity beacon dataset from the pilot study suitable only for assessing relationships between thermal preferences and temperature variables (rather than Gender, for instance) and for a preliminary assessment of the potential of machine learning techniques for improving prediction accuracy over existing static thermal comfort models. Table 1. Demographic Data Summary for Proximity Beacon Dataset Gender Male Female Not Declared

N Participants 25 21 6

N Responses 242 51 15

User-Type Undergraduate Graduate Staff Faculty Not Declared

19 26 2 3 2

126 82 2 92 6

Age 18-24 25-34 35-44 Not Declared

36 9 4 3

178 16 103 11

All spaces used in the study had functioning mechanical HVAC systems in operation during the pilot study. Data identifying the operational (i.e. heating, cooling) mode and temperature setpoint range for all spaces were not collected during the study. While detailed operational data for each room is desirable information to place subjective feedback in context, it was not possible to

collect this information systematically for all spaces within the timeline and resources of the study. Therefore, a balance point temperature estimate is applied (Section 3.1) to estimate operational mode.

2.6 Data Preparation A proximity beacon dataset was prepared for analysis by merging data sets 1, 2, and 3. A webportal dataset was prepared by merging data sets 3 and 4. The open-ended text field dataset (dataset 5) was not analyzed in the present study. For analysis purposes, the original 13-level thermal preference scale (ranging from -3 °C to +3 °C in 0.5 degree increments) was remapped to 7-level outcome variable (binned into 1 Deg. C increments) and also to a 3-level Thermal Preference outcome based on the following assumptions: Virtual Thermostat adjustment of 0 is ‘no change’; < 0 is ‘prefer cooler’; and > 0 is ‘prefer warmer’. To compare thermal preference results against the ASHRAE PMV model, the Center for the Built Environment (CBE) Thermal Comfort Tool was used [40] to calculate PMV outcomes based on an assumption that operative temperature is equal to measured air temperature, and static values of (air velocity = 0.1 m/s, humidity = 50%, metabolic rate = 1.2 met, clothing insulation = 0.6). In the present study, it is assumed that the difference between air temperature and operative temperature in the spaces is negligible, based on the hypothesis that indoor surfaces and the mass of air in the space are close to uniform temperatures, and based on prior field research which supports this hypothesis [41]. The PMV outcomes were then converted into 3 thermal preference levels based on the following assumptions: PMV < 0.5 & PMV > -0.5 is “no change”; PMV > 0.5 is “prefer cooler”; and PMV < -0.5 is “prefer warmer”, following the approach used in [27], and [42]. To examine the potential relationship, if any, between user subjective responses and outdoor air temperature, 5 additional predictor variables were added to the study dataset, which consist of, 1) Mean Daily Outdoor Temp., 2) Max Daily Outdoor Temp., 3) Thermal Discrepancy (In - Out), 4) Thermal Discrepancy (In - Mean Out) and 5) Thermal Discrepancy (In - Max Out). A description of all variables by type, class and with additional notes in provided in Table 2.

Table 2. Description of Variables in the Pilot Study Dataset. Variable Type

Class

Notes

User Virtual Thermostat Adj. (Votes)

Ordinal Factor

13 Levels: -3 to 3 in 0.5 Deg. C Increments

User Virtual Thermostat Adj. (Binned)

Ordinal Factor

7 Levels: -3 to 3 in 1 Deg. C Increments

User Thermal Preference

Ordinal Factor

3 Levels: Prefer Cooler, No Change, Prefer Warmer

User App ID

Factor

e.g. "0530E2F9-5059-4E5E-B0C3-D17201CD3634"

BLE Beacon ID

Factor

27 Levels: e.g. "050916f545a5aaab252d5991cff25c30"

Time Stamp

POSIXlt

Format: "2018-03-23 14:28:17"

ASHRAE PMV Thermal Preference

Ordinal Factor

3 Levels: Prefer Cooler, Neutral, Prefer Warmer

User Age Group

Factor

4 Levels: 18-24, 25-34, 35-44, Not Declared

User Gender

Factor

3 Levels: Male, Female, Not Declared

User Type

Factor

5 Levels: Undergrad, Grad, Faculty, Staff, Not Declared

Indoor Temperature

Numeric

Recorded by BLE beacon

Outdoor Temperature

Numeric

Interpolated from Local NOAA Station Weather Data

Mean Daily Outdoor Temp.

Numeric

Daily Mean from Local NOAA Station Weather Data

Max Daily Outdoor Temp.

Numeric

Daily Max from Local NOAA Station Weather Data

Thermal Discrepancy (In - Out)

Numeric

Indoor Temperature - Outdoor Temperature

Thermal Discrepancy (In - Mean Out)

Numeric

Indoor Temperature - Mean Daily Outdoor Temperature

Thermal Discrepancy (In - Max Out)

Numeric

Indoor Temperature - Maximum Daily Outdoor Temperature

Outcome Variables

Contextual Variables

Predictor Variables

3. Analysis Analysis of the pilot study datasets was conducted in two phases. The first was an exploratory phase to examine the distribution of user response data and the distribution of indoor and outdoor temperatures recorded concurrent with user responses to understand basic trends in the data. In the exploratory phase, we also fitted logistic models to the data in order to explore relationships between thermal preference and temperature variables. Results for this first phase are reported in Section 4.1 and Section 4.2 for the proximity beacon dataset and web-portal dataset respectively. The second phase of the analysis (described in detail in the following sections) applied machine learning to the proximity beacon dataset in order to generate data-driven models to predict the 7level user Virtual Thermostat Adjustment outcome as well as the more general outcome of Thermal Preference (i.e. ‘prefer cooler’; ‘no change’; and ‘prefer warmer’). The motivation behind this was to assess whether data collected using TrojanSense can be used to build models that predict user thermal preferences with higher accuracy than the static ASHRAE PMV model. Such models could potentially be used to better estimate subjective impacts of potential temperature setpoint changes or even to drive real-time automated adjustments of temperature

setpoints using live temperature data. Given the small size of our pilot data set, the models in this paper should be understood as a proof of concept rather than as systems that we recommend being deployed in practice without further improvement. 3.1 Machine Learning Ordinal logistic regression modelling was applied as a machine learning technique to generate predictive models. Ordinal logistic regression is a statistical method commonly used to predict the outcome of an ordered dependent variable (e.g. a Likert scale user response) given one or more independent variables. The proportional odds logistic regression (polr command) from the MASS package in R was used to build the models [43]. The approach involved partitioning the dataset into a training dataset and testing dataset using random sampling. Models were built using the training dataset and evaluated on the test dataset. The training dataset consisted of 75% of the study dataset and the test dataset consisted of the other 25%. The dependent variable in all models was the 7-level Virtual Thermostat adjustment (i.e. “Virtual Thermostat Adj. (binned),” Table 2). Consequently, a given model could be applied to predict the desired temperature adjustment (ranging from -3 to +3 °C in 1 °C increments), as well as interpreted to predict the more general user Thermal Preference response (i.e. ‘prefer cooler,’ ‘no change,’ and ‘prefer warmer’). As independent variable, Thermal Discrepancy (In – Out) was used, which is the discrepancy between the measured indoor air temperature sensed at the beacon and the local outdoor air temperature (from the on-campus NOAA weather station). In addition, in one of the models, the Indoor Temperature was used in addition to Thermal Discrepancy (In – Out). This more sophisticated model contains two logistic regression models, one trained on those responses for which the outdoor temperature was under a certain threshold and one trained on those responses for which it was above the threshold. The exact threshold value of 14 °C (57.2 °F) was learned from within a range of plausible balance points (i.e. 12 - 18 °C) so as to minimize misclassification rate on the training data. The accuracy of the logistic models was also compared to the accuracy of predictions made by the ASHRAE PMV model and by a simple baseline model which always predicts the most frequent class (i.e. most frequent user response) seen in the training data. 3.2 Model Evaluation For the proximity beacon dataset, the evaluation of a model was conducted by applying the model trained on the training dataset to the test dataset. The accuracy of each model was evaluated as the percentage of incorrect predictions on the test dataset and reported as the misclassification rate (0 – 100%), where lower numbers indicate more accurate models. The misclassification rate was calculated separately for both the 7-level Virtual Thermostat Adjustment and user Thermal Preference outcome variables. The probability value (p-value) was calculated as an additional indicator to evaluate model accuracy.

4. Results 4.1. Exploratory Analysis of the Proximity Beacon Dataset Fig. 4 – 8 present the results for the exploratory phase of analysis on the proximity beacon dataset. Fig. 4 describes the distribution of user responses in relation to concurrent indoor temperature measurements acquired from BLE beacons. Vertical lines at 23.0 °C and 26.3 °C indicate the ASHRAE PMV comfort zone calculated using the CBE Thermal Comfort Tool as described in Section 2.6. As shown in Fig. 4, the majority of responses were recorded at indoor temperatures below the ASHRAE PMV comfort zone. Fig. 5 shows the distribution of user responses in relation to outdoor air temperature. To estimate if a given space was in heating or cooling mode at the time of user response, an assumption of a 15.6 °C (60 °F) building balance point was made (vertical black line). Based on this assumption, the majority (67%) of the uer responses were reported when buildings were in cooling mode. Fig. 6 shows the distribution of user responses using the Virtual Thermostat. Red / warmer hues indicate that the user was too cool and preferred an increase in temperature. Blue / cooler hues indicate the user was too warm and preferred a decrease in temperature. Of all responses recorded, 72% indicated a preference for an increase in temperature, 14% were neutral, and 14% preferred a decrease in temperature.

80 (N = 308)

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Fig. 4. Distribution of user responses in relation to indoor air temperature acquired from proximity beacons. Note: 93.1% of observations (287 of 308) were recorded below the lower limit of the ASHRAE PMV comfort zone.

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Fig. 5. Distribution of user responses in relation to outdoor air temperature.

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Fig. 6. Distribution of user Virtual Thermostat Adjustment for proximity beacon dataset. Note: red / warmer hues indicate user preferred an increase in temperature. Blue / cooler hues indicate user preferred a decrease in temperature. Note: 72% of all responses indicated a preference to increase temperature.

Fig. 7 presents a scatterplot of color-coded user Virtual Thermostat Adjustment and indoor/outdoor temperature. The same color scale as used previously (e.g. Fig. 6) is used to code user subjective assessments. The diagonal grey line indicates the boundary where indoor temperature is equal to outdoor temperature. Visual exploration of the data showed that the majority of responses indicating preference for an increase in temperature (warm hues) were recorded at outdoor temperatures above the assumed balance point, with a noticeable cluster above the diagonal grey line (indicating responses recorded when outdoor temperature > indoor temperature). Importantly, for the (N= 207) responses where buildings were assumed to be in cooling mode, (N = 196) (94.7%) were recorded at indoor temperatures below the ASHRAE PMV comfort zone (23.0 °C) and 76.3% of responses (N=158 of the 207) indicated a preference to increase the temperature. Thus, a summary of the data serves as both an objective indicator of instances of overcooling (relative to conventional operational intent) as well as a subjective indicator of overcooling relative to occupant needs. Using linear regresison, indoor air temperature as a single variable was found to be an insignificant predictor of user Virtual Thermostat Adjustment when examining the proximity beacon dataset (p-value = 0.484) as shown in Fig. 8. 30

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Fig. 7. Scatter plot of user Virtual Thermostat Adjustment based on Indoor and Outdoor Temperature (°C). Note: red / warmer hues indicate user preferred an increase in temperature. Blue / cooler hues indicate user preferred a decrease in temperature.

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Fig. 8. Linear relationship of user Virtual Thermostat Adjustment predicted by Indoor Temperature (°C) for the proximity beacon dataset.

4.2. Exploratory Analysis of Web-Portal Dataset A total of 841 responses were collected across N=77 buildings via the web-portal during the study interval. Because the web-portal could be used to report from any space on campus, no infrastructure was available to pair responses with objective measures of indoor air temperature. Instead, outdoor air temperature was used to explore the data. Fig. 9 presents the distribution of web-portal user responses in relation to outdoor air temperature. The vertical black line indicates the assumed balance point of 15.6 °C (60 °F) for campus buildings. Similar to the proximity beacon dataset (Fig. 5), the majority (66%) of the responses collected via the web-portal were recorded when buildings were assumed to be in cooling mode. The majority of responses for the web-portal dataset (54%) indicated a preference to be warmer, 14% were neutral, and the remaining 32% indicated a preference to be cooler (Fig. 10). The web-portal dataset included a significantly larger proportion of responses indicating a preference to be cooler (32% (Fig. 10) vs. 14% (Fig. 6)) compared with the proximity beacon dataset as well as a larger proportion of responses at the two extremes of the Virtual Thermostat scale (19% and 17%, Fig. 10) compared with the proximity beacon dataset (1% and 8%) (Fig. 6). A “voting at the extremes” distribution

of responses (as exhibited in Fig. 10) is possibly due to greater motivation to participate to register discomfort than comfort. Fig. 11 presents a 3-factor ordinal logistic model applied to the web-portal dataset to predict user Thermal Preference (i.e. ‘prefer cooler’; ‘no change’; and ‘prefer warmer’) in relation to outdoor air temperature. Outdoor temperature was found to be a statistically significant predictor (p-value = 0.035). Notably, Fig. 11 shows that the predicted probability of a user to indicate a preference to be warmer is consistently greater than the probability of a preference to be cooler within the range of observed outdoor temperatures (i.e. 5 – 35 °C). For example, even on relatively warm days, when the outdoor air temperature is 26.7 °C (80 °F), the model predicts a 0.48 probability for “prefer warmer”, while only a 0.38 probability for “prefer cooler.” Overall, this result demonstrates that users of the web-portal are more likely to indicate overcooling even on warm days (i.e. up to 35 °C). (Heating Mode) 34%

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Fig. 9. Distribution of web-portal user responses in relation to outdoor air temperature.

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Fig. 10 Distribution of user Virtual Thermostat Adjustments for web-portal dataset. Note: red / warmer hues indicate user preferred an increase in temperature. Blue / cooler hues indicate user preferred a decrease in temperature.

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Fig. 11. Probability of user Thermal Preference based on Outdoor Temperature (p-value = 0.035) for the web-portal dataset. Vertical lines provide a statistical summary of all outdoor temperatures collected at the time of user responses (i.e. minimum, 1st quartile, mean, 3rd quartile, maximum).

4.3. Machine Learning Results Using Proximity Beacon Dataset Table 3 presents the misclassification rates on the proximity beacon dataset for the ASHRAE PMV comfort model, the most frequent class model, and the logistic regression models described in Section 3.1. The most frequent classes seen in the training data were “prefer warmer” for Thermal Preference and “+1 °C” for Virtual Thermostat Adjustment. For predicting Thermal Preference, the results suggest that machine learning models improve accuracy over the ASHRAE PMV comfort model (misclassification = 36%). Even the simple most frequent class model improves on the ASHRAE PMV comfort model, reducing the misclassification rate from 36% to 30%. The logistic model that uses Thermal Discrepancy (In Out) as independent variable does not achieve further improvements for predicting Thermal Preference. This is because it always assigns the highest probability to the response ‘prefer warmer’ within the range of values of Thermal Discrepancy (In – Out) in the data set, and hence

makes the same predictions as the most frequent class model. However, using both Indoor Temperature and Thermal Discrepancy (In - Out) to train one logistic model for cooling mode and one for heating mode (reported as ‘Logistic Model Tree’ in Table 3) based on an outdoor temperature threshold learned on the training set of 14 °C, (which we conjecture is the closest approximation to the unknown actual balance point), improves on the most frequent class model, resulting in an overall misclassification rate of 20%, which is a 16% improvement over the ASHRAE PMV comfort model. For predicting the more fine-grained Virtual Thermostat Adjustment, the logistic model based on Thermal Discrepancy (In – Out) improved over the most frequent class model (from 61% to 57%). The two-variable Logistic Model Tree led to a further reduction in misclassification rate for Virtual Thermostat Adjustment (to 52%). Fig. 12 presents a 7-level ordinal logistic regression model showing the probability of user Virtual Thermostat Adjustment (+/- Deg. C) predicted by Thermal Discrepancy (In – Out). Notably, Fig. 12 shows that as outdoor temperatures increasingly exceed indoor, the model predicts users to be more likely to request an increase in indoor temperature (by +2 or +3 °C). Similarly, as indoor temperatures increasingly exceed outdoor, the model predicts users to be more likely to request a decrease in temperature. Fig. 13 shows the probability of user Thermal Preference predicted by Thermal Discrepancy (In – Out). Similar to the results for the larger web-portal dataset (N=841) shown in Fig. 11, the model for the proximity beacon dataset predicts a significantly greater probability of a preference for an increase in temperature compared with a decrease over the range of data collected, including periods when the outdoor temperature exceeded indoor temperature, predicting a greater likelihood of overcooling compared to overheating. Table 3. Machine Learning Results Misclassification Rate (When Applied to Test Dataset) Model ASHRAE PMV

User Virtual Thermostat Adj. N/A

User Thermal Preference 36%

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Fig. 12. Probability of user Virtual Thermostat Adjustment (+/- °C) based on Thermal Discrepancy (In – Out), p-value p<0.001, for the proximity beacon dataset.

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Fig. 13. Probability of user Thermal Preference (+/- Deg. C) based on Thermal Discrepancy (In – Out), p-value = 0.00251, for the proximity beacon dataset. Vertical lines provide a statistical summary of all Thermal Discrepancy (In – Out) observations at the time of user response (i.e. minimum, 1st quartile, mean, 3rd quartile, maximum).

5. Discussion Key findings from analysis of the pilot study data are listed below and discussed in regard to how they can provide useful guidance to inform actionable control strategies and policies to improve thermal comfort and reduce HVAC energy consumption due to overcooling or overheating. The limitations of the study are then discussed in Section 5.5. 5.1. Overcooling / Overheating TrojanSense was found to be an effective mechanism for identifying and reporting the overcooling or overheating of spaces based on occupant subjective feedback. The most frequent use of both the mobile app and the web-portal was to report a subjective preference for an increase in temperature. Noteably, for the (N= 207) responses where buildings were assumed to be in cooling mode, (N = 196) 94.7% of all responses were recorded at indoor temperatures

below the ASHRAE PMV comfort zone (23 °C) and 76.3% of responses (N=158 of the 207) indicated a preference to increase the temperature. Thus, a summary of the data serves as both an objective indicator of instances of overcooling (relative to target indoor air temperatures) as well as an indicator of overcooling relative to occupant subjective preferences. Further, probabilistic models of user Thermal Preference (i.e. ‘prefer cooler,’ ‘no change,’ and ‘prefer warmer,’) generated separately for both the web-portal dataset (Fig. 11) and the proximity beacon dataset (Fig. 13) both predict a significantly greater probability of a user preference to be warmer rather than cooler within the range of observed temperatures, providing further evidence that the risk of overcooling exceeds overheating. The Campus Thermal Preference Map (Fig. 3), which is designed to help building management personnel navigate overcooling or overheating outcomes with room-level resolution across the campus, can help to quickly identify and prioritize problematic spaces, as well as to validate the effectiveness of retrofits or other interventions once completed. 5.2. Machine Learning Models Machine learning models generated from a randomly sampled training dataset (75% of proximity beacon dataset) were found to improve prediction accuracy relative to a static ASHRAE PMV model, which incorrectly predicted the Thermal Preference of occupants in 36% of the test dataset (the remaining 25% of proximity beacon dataset). The accuracy of machine learning models that used Indoor Temperature and Thermal Discrepancy (In – Out) were found to reduce the misclassification rate for Thermal Preference to 20% and to reduce the misclassification rate for Virtual Thermostat Adjustment from 61% to 52% (Table 3). This suggests that predictive machine learning models of user Virtual Thermostat Adjustment (e.g. Fig. 12) or the more general outcome of Thermal Preference (e.g. Fig. 13) have the potential to more accurately estimate the subjective impacts (if any) of various possible controls adjustments and to enable dynamic adjustment of temperature setpoints based on real-time temperature data that improves user satisfaction over static setpoint policies. 5.3. Discrepancy Between Indoor and Outdoor Temperature The discrepancy between indoor and outdoor temperature was found to be an important variable in predicting user responses. In support of adaptive theory [11-13], findings in this paper (e.g. Figs. 11-13) support the hypothesis that outdoor temperature is a significant factor that should be taken into account when predicting the thermal response of building occupants. An awareness of how occupant thermal preferences change in response to both outdoor temperatures and the discrepancy between indoor and outdoor temperature is particularly important for informing future campus-wide policies that go beyond specification of static seasonal (e.g. summer and winter) temperature setpoints to more dynamic, climate-and-occupant-aware strategies. Findings in this paper suggest that, rather than static annual or seasonal setpoint adjustments, future controls scenarios should apply daily or hourly setpoint adjustments which drift toward ambient outdoor temperature, similar to the approach demonstrated by Purdon et al. [35]. Over time, evidence demonstrating that occupants accept, and may even prefer, a greater range and seasonal variation in indoor temperatures can serve to inform the design of innovative new building

concepts that utilize environmental services provided by natural systems to create more dynamic, diverse and comfortable indoor environmental experiences. 5.4. Indoor Air Temperature as a Predictor of Occupant Thermal Preference Indoor air temperature as a single variable was found to be an insignificant predictor of occupant thermal preference when examining the full proximity beacon dataset (p-value = 0.484, Fig. 7). Therefore, the findings suggest that campus-wide policies and controls should be improved by incorporating greater awareness and responsiveness to additional factors beyond indoor air temperature, such as the discrepancy between indoor and outdoor temperature, as noted above. 5.5. Limitations While the pilot study demonstrated the feasibility of TrojanSense as an approach for obtaining greater insight into the impacts of thermal management practices on occupants, the limited scale of deployment (N=23 beacons) and low response in some individual spaces limited the capability to generate accurate machine learning models to explore thermal conditions and predict user preferences on a room-by-room level across all spaces. The machine learning models presented in this paper should be seen as a proof of concept rather than as the basis for deployment-ready systems. The need to recruit a sufficient number of participants from a representative sample of building occupants is a social/behavioral challenge faced by all participatory sensing applications. In the case of TrojanSense, this barrier may be addressed in subsequent studies via greater institutional coordination among units as well as with a greater level of coordination and engagement with motivated groups of campus users. In regard to the Campus Thermal Preference Map (Fig. 3), the marking of individual rooms on the 2-dimensional map proved feasible for a small deployment of beacons, but would quickly become difficult to interpret if all rooms within a multi-level building were individually marked. We plan to address this limitation by developing a web-interface for 3-dimensional building representation. Facility management personnel will likely be averse to relying on objective measures of air temperature from proximity beacons until sensor accuracy can be improved beyond the accuracy of sensors currently embedded in available BLE beacon technology (+/- 2 Deg. C, Section 2.2). The concern over sensor accuracy is not unique to proximity beacons. Thermostats may drift out of calibration over time, or be exposed to thermal conditions that are not representative of the majority of the space (or spaces) they are intended to monitor. The approach used in this study was to “repurpose” an emerging technology developed for commercial retail applications and use it for a research application. Future participatory sensing applications may benefit from purposebuilt BLE platforms which integrate proximity sensing features with research grade IEQ sensors. Estimation of the thermal comfort range using the PMV calculation was limited by lack of data on participation clothing level, metabolic rate, and the assumption that the difference between room air temperature and operative temperature was negligible. The research team plans to consider incorporating additional survey questions addressing the two behavioral factors (clothing and activity level) in future studies, which may be useful in further explaining variations in thermal preference between participants. However, it should be emphasized that the primary objective of the TrojanSense project is not to enable more precise estimation of the

parameters needed for the PMV model as a means to predict thermal sensation of theoretical building occupants, but rather as a service to collect subjective assessments directly from real building occupants. 6. Conclusions TrojanSense presents a practical, low-cost and scalable participatory sensing framework to inform actionable strategies aimed at improving thermal comfort and reducing unnecessary energy use associated with the over-conditioning of spaces beyond the requirements of their occupants. This paper demonstrated the application of Bluetooth Low Energy (BLE) proximity beacons for participatory thermal sensing to inform decision making with room-level spatial resolution. Results showed improved comfort prediction over the static ASHRAE PMV model. Findings demonstrated a significant relationship between subjective thermal preference and the discrepancy between indoor and outdoor temperature, providing support for future “drift” control strategies which allow room temperatures to follow daily changes in outdoor temperatures as an alternative to controls based on static setpoints. In addition to the potential to improve thermal comfort outcomes, setpoint adjustments that bring indoor temperatures closer to outdoor temperatures have the potential to reduce the carbon emissions associated with the electricity consumption of HVAC systems. Additional studies incorporating drift control strategies and data-driven extended temperature setpoint ranges are needed to quantify the energy and carbon savings potential of participatory sensing. TrojanSense presents a promising approach to help enable large institutions to engage with the campus community to inform thermal management practices. As an alternative to a static and universally-applied indoor climate, the most significant technological improvement is the potential to enable a variety of indoor climates which are controlled with an awareness of the diverse needs and preferences of campus users and which are responsive to climate and the unique characteristics of each thermal zone. Acknowledgements This research was supported in part by a grant from the USC Green Engagement Fund and a USC Undergraduate Research Associates Program (URAP) award. The authors would like to acknowledge the work of USC students: Shuhui He, Zhengao Dong, and Danyang Zhang. The authors are additionally grateful for the support from the USC Office of Sustainability (Ellen Dux) and USC Facilities Management Services (Zelinda Welch).

References [1] U.S. Department of Energy. 2011 Buildings Energy Data Book (CH 3). https://ieer.org/resource/energy-issues/2011-buildings-energy-data-book/ Last accessed 9/16/2019. [2] University of Southern California's Living Lab Utility Data Interactive Board https://buildingos.com/s/usc/storyboard6803/?chapterId=40473 Last accessed 9/16/2019. [3] U.C. Berkeley Energy Dashboard https://engagementdashboard.com/ucb/ucb Last accessed 9/16/2019. [4] Stanford University Campus Energy Dashboards http://sustainable.stanford.edu/campus-action/campus-performance Last accessed 9/16/2019. [5] U.C. Davis Campus Energy Education Dashboard (CEED) https://ceed.ucdavis.edu/ [6] Pritoni M, Salmon K, Sanguinetti A, Morejohn J, & M Modera. Occupant thermal feedback for improved efficiency in university buildings. Energy and Buildings 144 (2017) 241–250. [7] P.O. Fanger, Thermal Comfort: Analysis and Applications in Environmental Engineering, Danish Technical Press, Copenhagen, 1970, pp. 1970. [8]. ANSI/ASHRAE, ANSI/ASHRAE 55-2013: Thermal environmental conditions for human occupancy. American Society of Heating, Refrigerating and Air-Conditioning Engineers, Atlanta, GA, US, 2013. [9] Mendell, M. and Mirer AG. (2009). Indoor Thermal Factors and Symptoms in Office Workers: Findings from the U.S. EPA BASE Study. Indoor Air. 2009 Aug;19(4):291-302. doi: 10.1111/j.1600-0668.2009.00592.x. Epub 2009 Mar 13. [10] Gail Brager, Hui Zhang & Edward Arens (2015). Evolving opportunities for providing thermal comfort, Building Research & Information, 43:3, 274-287, DOI: 10.1080/09613218.2015.993536 [11] Brager, G. S.; & de Dear, R.(2001). Climate, comfort, & natural ventilation: a new adaptive comfort standard for ASHRAE standard 55. UC Berkeley: Center for the Built Environment. Retrieved from: http://escholarship.org/uc/item/2048t8nn [12] Brager, G.S., and R.J. de Dear. 2001. Climate, comfort & natural ventilation: a new adaptive comfort standard for ASHRAE Standard 55. Proceedings, Moving Thermal Comfort Standards into the 21st Century, Windsor, UK. April. http://www.escholarship.org/uc/item/2048t8nn

[13] Brager, G.S., and R.J. de Dear. 1998. Thermal adaptation in the built environment: a literature review. Energy and Buildings, Vol. 27, No. 1, pp. 83-96. [14] Licina et al. Development of the ASHRAE Global Thermal Comfort Database II. Building and Environment. Volume 142, September 2018, Pages 502-512. [15] Cheung T., Schiavon S., Parkinson T., Li P., Brager G. Analysis of the accuracy on PMV – PPD model using the ASHRAE Global Thermal Comfort Database II. Building and Environment. Volume 153, 15 April 2019, Pages 205-217. [16] Kazanci, O. B., Olesen, B. W. (2013). The Effects of Set-Points and Dead-Bands of the HVAC System on the Energy Consumption and Occupant Thermal Comfort. 9th Windsor Conference: Making Comfort Relevant, At Windsor, UK. [17] Ghahramani, A., Zhanga, K., Duttaa, K., Yanga, Z., Becerik-Gerber, B. (2015). Energy savings from temperature setpoints and deadband: Quantifying the influence of building and system properties on savings. Applied Energy. doi: 10.1016/j.apenergy.2015.12.115. [18] Hoyt, T., Arens, E., Zhang, H. 2015. Extending air temperature setpoints: Simulated energy savings and design considerations for new and retrofit buildings. Building and Environment. Volume 88, June 2015, Pages 89–96. [19] Li P., Froese T., and G. Brager. 2018. Post-occupancy evaluation: State-of-the-art analysis and state-of-the-practice review. Building and Environment 133 (2018) 187–202 [20] ISO, ISO 10551:2019, Ergonomics of the thermal environment - Assessment of the influence of the thermal environment using subjective judgment scales. International Standard Organization, Geneva, 2019. [21] Jazizadeh, F., Kavulya, G., Klein, L., and Becerik-Gerber, B. (2011). Continuous Sensing of Occupant Perception of Indoor Ambient Factors. Computing in Civil Engineering (2011): pp. 161-168. [22] V. Erickson, A. Cerpa, Thermovote: participatory sensing for efficient building HVAC conditioning, in: In Proceedings of the Fourth ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings (BuildSys ‘12),ACM, New York, NY, USA, 2012, pp. 9–16, http://dx.doi.org/10.1145/2422531.2422534. [23] A. Hang-yat Lam, Y. Yuan, D. Wang, An occupant-participatory approach for thermal comfort enhancement and energy conservation in buildings, in: In Proceedings of the 5th International Conference on Future Energy Systems(e-Energy ‘14), ACM, New York, NY, USA, 2014, pp. 133–143, http://dx.doi.org/10.1145/2602044.2602067.

[24] A. Ghahramani, F. Jazizadeh, B. Becerik-Gerber, A knowledge based approach for selecting energy-aware and comfort-driven HVAC temperature set points, Energy Build. 85 (December) (2014) 536–548, http://dx.doi.org/10.1016/j.enbuild.2014.09.055, ISSN 0378-7788. [25] L. Baker, T. Hoyt, Control for the people: how machine learning enables efficient HVAC use across diverse thermal, in: In Proceeding for American Council for an Energy-Efficient Economy Summer Study on Energy Efficiency in Buildings, Asilomar, CA, August 2016, 2016. Retrieved from aceee.org/files/proceedings/2016/data/papers/8_57.pdf [26] K. Konis, M. Annavaram, The occupant mobile gateway: a participatory sensing and machine learning approach for occupant-aware energy management, Build. Environ. 118 (June) (2017) 1–13, http://dx.doi.org/10.1016/j.buildenv.2017.03. 025 ISSN 0360-1323. [27] Kim J., Zhou Y, Schiavon S, Raftery P, Brager G. Personal comfort models: Predicting individuals' thermal preference using occupant heating and cooling behavior and machine learning. Building and Environment. Volume 129, 1 February 2018, Pages 96-106. [28] Schott, M., Scheib, J., Long, N., Fleming, K., Benne, K., and Brackney, L. Progress on Enabling an Interactive Conversation Between Commercial Building Occupants and Their Building To Improve Comfort and Energy Efficiency: Preprint. United States: N. p., 2012. [29] Konis, K. Leveraging ubiquitous computing as a platform for collecting real-time occupant feedback in buildings. Intelligent Buildings International, Volume 5, Issue 3, 2013, pages 150161. [30] Lee, K., J. Braun, S. Fredrickson, K. Konis, and E. Arens, 2007. Testing of Peak Demandlimiting Using Thermal Mass at a Small Commercial Building. Demand Response Research Center Report, LBNL, July, 38pp. [31] Shin E., Yus R., Mehrotra S., and N. Venkatasubramanian. 2017. Exploring Fairness in Participatory Thermal Comfort Control in Smart Buildings. Proceedings of the 4th ACM International Conference on Systems for Energy-Efficient Built Environments. Delft, Netherlands. [32] Wang Z., Parkinson T., Li P., Lin B., and T. Hong. 2019. The Squeaky wheel: Machine learning for anomaly detection in subjective thermal comfort votes. Building and Environment 151 (2019) 219–227. [33] Kingma B. and W. van Marken Lichtenbelt. 2015. Energy consumption in buildings and female thermal demand. Nature Climate Change. 2015;5:1054-6. [34] D.A. Winkler, A. Beltran, N.P. Esfahani, P.P. Maglio, A.E. Cerpa, FORCES: feedback and control for occupants to refine comfort and energy savings, Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp’16), ACM, New York, NY, USA, 2016, pp. 1188–1199, http://dx.doi.org/ 10.1145/2971648.2971700.

[35] S. Purdon, B. Kusy, R. Jurdak, G. Challen, Model-free HVAC control using occupant feedback, Second IEEE International Workshop on Global Trends in Smart Cities 2013 (2013). [36] Sanguinetti A, Pritoni M, Salmon K., Meier A., & J. Morejohn. Upscaling participatory thermal sensing: Lessons from an interdisciplinary case study at University of California for improving campus efficiency and comfort. Energy Research & Social Science 32 (2017) 44–54. [37] U.C. Davis TherMOOmap. https://thermoostat.ucdavis.edu/ Last accessed: 9/16/2019. [38] Estimote Website. What kind of sensors do Estimote Beacons have? https://community.estimote.com/hc/en-us/articles/216882758-What-kind-of-sensors-doEstimote-Beacons-haveAccessed 11/13/2019 [39] Onset Website. HOBO Temperature/Relative Humidity/2 External Channel Data Logger. https://www.onsetcomp.com/products/data-loggers/u12-013 Accessed 11/13/2019 [40] Hoyt Tyler, Schiavon Stefano, Piccioli Alberto, Cheung Toby, Moon Dustin, and Steinfeld Kyle, 2017, CBE Thermal Comfort Tool. Center for the Built Environment, University of California Berkeley, http://comfort.cbe.berkeley.edu/ [41] Walikewitz N., Janicke B., Langner M., Meier F., Endlicher W. (2015). The difference between the mean radiant temperature and the air temperature within indoor environments: A case study during summer conditions. Building and Environment 84 (2015) 151e161 [42] A. Ghahramani, C. Tang, B. Becerik-Gerber, An online learning approach for quantifying personalized thermal comfort via adaptive stochastic modeling, Building and Environment. 92 (2015) 86–96. doi:10.1016/j.buildenv.2015.04.017. [43] MASS package in R. https://cran.r-project.org/web/packages/MASS/MASS.pdf Last accessed: 9/16/2019.

Highlights • Buildings lack a mechanism for feedback on occupant thermal comfort • A scalable participatory sensing framework was developed and tested • Proximity sensing is used to automate subjective surveys with room-level resolution • Responses are analyzed and mapped on Campus Thermal Preference Map • Data-driven comfort models improve accuracy over a static PMV model

Declaration of interests ☐ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☒The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Kyle Konis is an employee of the University of Southern California (USC) (assistant professor). Simon Blessenohl and Naman Kedia are current students at USC, and Vishal Rahane was a student at the time the research being reported was conducted. Naman Kedia received funding from a USC Undergraduate Research Associates Program (URAP) award and Naman Kedia and Vishal Rahane received funding from Prof. Konis’ faculty research development account. Equipment used in the research (Estimote proximity beacons) were purchased using funds from an award received by Simon Blessenohl from the USC Green Engagement Fund.