Developing a Thermal Comfort Report Card for Building

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Procedia Engineering 118 ( 2015 ) 675 – 682

Peer-review under responsibility of organizing committee of the International Conference on Sustainable Design, Engineering and Construction 2015 doi: 10.1016/j.proeng.2015.08.502

ScienceDirect

International Conference on Sustainable Design, Engineering and Construction

Developing a Thermal Comfort Report Card for Building

Moatassem Abdallah

a

*, Caroline Clevenger

a

, Mani Golparvar-Fard

b

a_{University of Colordo Denver, 1200 Larimer Street, North classroom, Devver Colorado, 80217, USA} b_{University of Illinois at Urbana Champaign, 205 N. Mathews Ave., Urbana, Illinois, 61801, USA}

Abstract

Buildings consume 40% of total energy in the United States and approximately 48% of which is consumed by Heating Ventilation and Air Conditioning (HVAC). This highlights the importance of developing robust and dynamic Building Monitoring Systems (BMS) that are capable of providing the optimal operation of HVAC systems in terms of maximizing thermal comfort of building occupants while minimizing energy consumptions. Numerous empirical studies have demonstrated that occupant behavior is a key factor underlying energy consumption in existing buildings. However, few if any reliable data sets exist documenting precise human activities and their associated occupant comfort levels within buildings. Furthermore, little if anything is known about how this information directly relates to building energy performance. This research documents on-going development of software prototype tools for modeling thermal comfort in buildings based on real-time occupant and building systems data. The outcomes help building owners to identify areas that require improvements with regard to thermal comfort with broader impacts that improve occupant productivity, comfort, and well-being. The primary technical contribution is to model human comfort on the building level based on actual occupant usage, in order to identify and target energy efficiency measures that optimize energy usage according to comfort rather than maximum energy savings alone. Future research will synthesize building occupant and sensor data to support regression analysis that may identify the correlation of the reported thermal comfort, activities of building occupants, and building conditions. Such data may also be used to develop algorithms for controlling interior lighting, exhaust fans, ventilation, and HVAC temperature set points that optimize comfort while minimizing energy demands.

Peer-review under responsibility of organizing committee of the International Conference on Sustainable Design, Engineering and Construction 2015.

Keywords: T hermal Confort, Building Occupants, Energy Perfromance

* Corresponding author. Tel.: +1-303-556-5287; fax: +1-303-556-2368.

E-mail address: moatassem.abdallah@ucdenver.edu

Peer-review under responsibility of organizing committee of the International Conference on Sustainable Design, Engineering and Construction 2015

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1.Introduction

Building sector accounts for forty percent of primary energy consumption in the United States while operational energy consumption within buildings is the largest contributing factor. Numerous empirical studies have demonstrated that occupant behavior is a key factor underlying energy consumption in existing buildings. However, few if any reliable datasets exist documenting precise human activities and their associated occupant comfort levels within buildings. Furthermore, little is known about how this information directly relates to building energy performance. Several studies have been developed to understand occupant behavior and thermal comfort as well as their impact on building energy consumption. These include evaluating the impact of occupant behavior on energy consumption of private offices using building simulation [1], evaluating the accuracy of occupancy modeling using various ambient sensor variables and identifying the contribution of each sensor variable on the modeling results [2], understanding occupant behavior in an office building by sensing daily activities and their interaction with building energy devices which identified 38% potential energy savings due to turning off appliances when not in use [3], demonstrating the impact of occupant behavior uncertainties on building energy consumption using parametric simulation of commercial and residential buildings which showed s ignificant impact on annual energy usage [4,5], developing a framework to understand comfort profiles of building occupants in the HVAC control loop and control

HVAC system based on occupants’ personalized comfort profiles which showed a proportional controller algorithm

that is capable of keeping the thermal zones’ temperatures in the ranges of preferred temperatures [6,7], and developing an agent based approach to account for the divers and dynamic energy consumption patterns among occupants in commercial buildings which showed 25% variation in the predicted energy consumption of an office building [8,9].

Furthermore, several models and tools have been developed to evaluate and improve thermal comfort of building occupants, including a thermal comfort tool by center for the built environment that is capable of evaluating comfort of building occupants according to ASHRAE standard-55 which leads to low-energy designs [10]; Multi-Agent Comfort and Energy Simulation (MACES) to model alternative management and control of building systems and occupants to reduce building energy consumption while maintaining occupant comfort [11]; multi-agent based intelligent control system for achieving effective energy and comfort management in building environment which is capable of facilitating the interaction between building and occupants [12]; and occupancy monitoring system that is capable of detecting indoor temperature, humidity, CO2 concentration, light, door status, sound and motion in an effort of supporting demand driven HVAC operations [13,14].

Despite the significant contribution of the existing studies and models of understanding and improving thermal comfort of building occupants while minimizing energy demand, there is limited or no research that focused on evaluating the overall thermal comfort performance of buildings in a scalar matrix based on occupant feedback and building systems data. This paper discusses the on-going development of a thermal comfort report card and software prototype tools for modeling thermal comfort in buildings based on real-time occupant feedback and building systems data. A mobile application is being developed for commodity smartphones to automatically gather data on occupant behavior at the activity level, their clothing, as well as perceived thermal comfort. While recognition of activities will be done automatically, the building occupants will use the developed smartphone application to report their clothing and level of thermal comfort in each room over a period of time. The results of thermal comfort analysis is delivered in a building “thermal-comfort report card,” which will present room-by-room comfort levels through time and a building thermal performance annual average, based on actual usage patterns. Simultaneously, indoor environmental conditions is collected through the use of various building sensors over a period of time. The integrated data from building occupants and sensors is being used to develop a metric for evaluating thermal comfort of building occupants. In the future, this metric will be validated using collected feedback data related to perceived comfort.

2.Assessing thermal comfort of building occupants

Most buildings currently rely on the industry standards , and assume that thermal comfort in buildings is being met as according to American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE) standard

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55. ASHRAE standard 55 uses Predicted Mean Vote (PMV) as a thermal comfort index that assesses and ensures satisfactory environmental conditions during building occupancy. The PMV is designed to quantify the average building occupant comfort perception based on two assumptions clothing levels, and metabolic rates , coupled with four other indoor conditions including air temperature, humidity, radiant air temperature, and air speed to predict thermal comfort in buildings. Collectively, these six parameters are adjusted to ensure that a specific minimum level of Predicted Percentage Dissatisfied (PPD) is achieved in the design of buildings.

Despite the advancements in Building Management Systems (BMS) and the recent research in adaptive building control systems [15–19], the PMV index and air-quality levels are the most common techniques to maintain occupant comfort in buildings without taking into consideration the actual occupant preferences and their comfort levels. A new thermal comfort report card is proposed in this research to evaluate the thermal comfort of building occupants which will present room-by-room comfort levels through time and a building thermal performance annual average, based on actual usage patterns. This system sets the comfort levels of building occupants based on the six parameters identified by the ASHRAE Standard 55 in 2010 [20]. Currently, however, ASHRAE Standard 55 evaluates a space in a binary fashion, as either meeting thermal comfort requirements (falling within the prescribed region on the psychometric chart) or not [10]. The new thermal comfort report card requires a scalar metric. Therefore, comfort level boundaries are identified in the temperature-relative humidity chart based on these six parameters, which are divided into 11 zones of different comfort levels. These 11 zones are represented with a score that ranges from 0.0 to 10.0, as shown in T able 1. The highest score (10) represents the most comfort zone that is identified by the ASHRAE Standard 55 (2010) with a zero PMV value which resulted in 5% PPD [20]. The comfort score decreases as the combination of drybulb temperature and relative humidity deviate from the identified line of zero PMV. The lowest score (0.0) is represented with PMV values of (≥2.50) and (≤-2.50) that results in PPD higher than 93.5%. For example, the comfort zone of ASHRAE Standard 55 for indoor conditions of air velocity 0.1 m/s, clothing 0.5 clo, and metabolic rate 1.1 met is highlighted with light green zone which corresponds to PMV values of (0.5) and (-0.5) as shown in Figure 1. Once these four factors are set (determining the frame of the comfort zone according to various dimensions), the 11 zones of thermal comfort can be identified for these environmental conditions by identifying the drybulb temperature based on various relative humidity values (0–100%) that result in PMV values as shown in T able 1. It should be noted that, the comfort zone that is identified by the ASHRAE Standard 55 [10,20] is represented in this model with score that ranges from 8.0 to 10.0, as shown in Figure 1. Based on the identified 11 zones, a comfort score can be identified for a specified indoor conditions of the drybulb temperature and relative humidity, again, assuming prescribed values for the other four factors. For example, a drybulb temperature of 26°C with 55% relative humidity, as shown in Figure 1 for point A, corresponds to a comfort score of 9.0.

T able 1. T hermal comfort scores and their associated PMV and PPD

Score PMV (+ve) PMV (-ve) PPD

(%) 10 0 0 5 9 0.25 -0.25 6.3 8 0.50 -0.50 10.0 7 0.75 -0.75 16.9 6 1.00 -1.00 26.3 5 1.25 -1.25 37.9 4 1.5 -1.5 51.1 3 1.75 1.75 64.6 2 2.00 -2.00 76.9 1 2.25 -2.25 86.8 0 >2.50 <-2.50 >93.5

The new thermal report card is being developed for buildings using this scalar measure to represent thermal comfort of building occupants. As such, it is capable of presenting room-by-room comfort levels through time and a building thermal performance annual average. Specifically, this report card uses the aforementioned scoring technique to identify the comfort score (as opposed to the binary “comfortable” “uncomfortable” assessment) for each occupant in the building. A room score is then calculated based on the average comfort score of each occupant in the room.

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This room score will account for the recordings of the building data and environmental conditions to calculate the comfort score periodically. An average comfort score is calculated for each occupant in the room which represent the comfort level of the building occupant over a period of time. The overall Building Thermal Comfort Score (BTCS) is calculated based on the room area (Ai), building area (A), and thermal comfort room score (RSi), as

shown in Equation (1). This overall comfort score represent the comfort level of the building based on the comfort score in each room/space that is calculated over a period of time and the size of the room as compared to the building surface area. For example, a thermal comfort report card can be developed and visualized for an office, as shown in Figure 2. This report card shows the rooms/spaces that have the highest comfort levels such as the conference room and office 3, and the rooms/spaces that have the lowest comfort levels such as office # 1 and office # 6 which are in urgent need of improving their comfort levels. The overall comfort score of the building is calculated as 6.5 out of 10 which can be increase initially by improving comfort levels in office # 1 and office # 6. This can be performed by analyzed the gathered data of the building and the environmental conditions and address the building needs to improve the thermal comfort in areas of poor comfort scores. The recommendations of the thermal comfort report card to improve the overall thermal comfort score of the building can be sent to the building occupants using the smartphone application.

Figure 1. T hermal comfort scores of building occupants as assessed according to the frame set by addition four ASHRAE Standard 55 factors

BTCS = σ ஺೔

஺כ ܴܵ௜ ௡

௜ୀଵ (1)

where n is number of rooms/spaces in the building, ܣ_௜ is surface area of room/space ݅, ܣ is building surface area,

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Figure 2. Example thermal comfort report card visualization

3.Measuring thermal comfort

Thermal comfort report card scores and evaluates the comfort performance of buildings based on the six parameters that are identified by the ASHRAE Standard 55 in 2010 [20]. The data of these parameters will be collected based on BMS, sensors, and smartphone applications. The drybulb temperature and humidity will be collected using temperature and humidity sensors that are installed in each room of the building and are connected to the BMS. The temperature and humidity in each room will be measures and recorded periodically (e.g. every 1 hr.) in a system database. The radiant temperature will be measures and recorded using radiant temperature sensors that are installed near to building occupants. Air flow meters will be used to measure air speed in each room of the building. The values of metabolic rates and clothing are identified based on the ASHRAE Standards. The metabolic rates can be identified based on the activity level that are categorized in the ASHRAE Standards, including resting, walking, office activities, driving/flying, miscellaneous occupational activities, and miscellaneous leisure activities. These activities results in metabolic rate that ranges from 0.7 met units for sleeping to 8.7 met units for wrestling [20]. The building occupant will report their metabolic rate by selecting their type of actives using the smartphone application. The clothing parameter of thermal comfort can be identified based on the clothing insulation values for typical ensembles in the ASHRAE Standards. The clothing insulation is divided into six sections with different insulation values, including trousers, skirts/dresses, shorts, overalls/coveralls, athletic, and sleepwear which ranges from o.57 clo for trousers with shorsleeve shirt to 1.3 clo for suite jacket, long underwear bottoms, long sleeve sweater, t-shirt, trousers, and long-sleeve shirt [20]. Building occupants will report their clothing insulation values by selecting garments using the smartphone application. The collected data using BMS and sensors will be integrated with the smartphone data to calculate the comfort score for each room in the building and according the BTCS. Furthermore, building occupants will report their comfort level using the smartphone application based on score that ranges from 0 to 10 where 0 represent the worst level of thermal comfort and 10 represent the highest level of thermal comfort. The reported feedback from building occupants will be used to validate the developed thermal comfort metric and improve its accuracy and performance.

R oom S core (ܴܵͳ)* = 3.2 ܴܵʹ = 7.6 ܴܵ͵ = 8.6 ܴܵͶ = 7.6 ܴܵͷ = 7.6 ܴܵ͸ = 2.0 ܴܵ͹ = 5.4 ܴܵͺ = 7.0 ܴܵͻ= 7.0 ܴܵͳͲ = 6.1 ܴܵͳ͵ = 6.5 ܴܵͳͶ = 9.0 C onference R oom ܴܵͳͳ = 5.6 ܴܵͳʹ = 7.0

O ffice # 1 O ffice # 2 O ffice # 3 O ffice # 4 O ffice # 5 O ffice # 6 O ffice # 7 J anitor Work A rea Lobby V estibule R oom area (ܣͳሻ = ܮͳכ ܹͳ ܹͳ ܮͳ

A is building area excluding unoccupied spaces such as vestibule entrances and corridors. O ffice # 8

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4.Summary and conclusions

This paper presents a new approach to evaluating human comfort within building s. The developed thermal-comfort report card evaluates overall and discrete thermal comfort for building occupants based on room-by-room comfort levels through time and a building thermal performance annual average. The proposed system identifies the thermal comfort of building occupants based on the six parameters identified by the ASHRAE Standard 55 including drybulb temperature, relative humidity, radiant mean temperature, air velocity, metabolic rate, and clothing level. These parameters are measures in buildings using BMS and sensors along with smartphone application. The reported data of these parameters are used to identify 11 zones of comfort levels which are represented with a score that ranges from 0.0 to 10.0 where 0.0 represent the worst thermal comfort score and 10.0 resents the best thermal comfort score. A room comfort score is calculated based on the average comfort score of all occupants in the room. The overall thermal comfort score of a building is then calculated based on the room comfort score, room surface areas, and building surface area. The performance of the developed metric fo r identifying thermal comfort of building occupants will be evaluated, analyzed, and improved by allowing building occupants to report their actual thermal comfort levels with a similar score that ranges from 0.0 to 10.0. The reported feedback from building occupants will validate the developed thermal comfort metric and improve its performance. Furthermore, t he analysis of the reported thermal comfort scores along with the developed metrics will provide feedback on the actual performance of ASHRAE Standard 55 based on occupant feedback and preferences.

The outcome of this research will help building owners and operators to identify the thermal comfort performance of their buildings and identify areas that require improvements of thermal comfort. This can result in broader impact of improving productivity, comfort, and well-being as well as reducing absenteeism of building occupants. This research is also expected to lead to more broad and profound impacts including 1) a new performance metric (thermal comfort score) to compare building performance across campuses or building types (residential, commercial, industrial etc.); 2) prototype assessment tools leveraging cross -validation of human perception and sensor data; and 3) demonstration of the potential impact of thermal comfort on building energy performance. Furthermore, the outcome of this research can be used to provide information on how to maximize thermal comfort while minimizing energy consumption by implementing sustainable measures for controlling interior lighting, exhaust fans, ventilation, and HVAC temperature set points. In the future, such research can also be used by US Green Building Council to facilitate the accreditation of the thermal comfort credit area in the Leadership in Energy and Environmental Design (LEED) rating system for existing buildings.

Acknowledgement

This research is based on an ongoing project that is funded by Rexel Foundation. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of Rexel Foundation.

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