Conference PaperPDF Available

A Real-Time Approach to Evaluate Occupants’ Thermal Comfort in the Indoor Environment

Al-Adhami M., Wu S. and Delzendeh E. (2020). "A real-time approach to evaluate occupants' thermal
comfort in the indoor environment" In:
Proc. 37th CIB W78 Information Technology for Construction
(CIB W78), São Paulo, Brazil, pp. 477-487. DOI:
Mustafa Al-Adhami 1, Song Wu2 and Elham Delzendeh 3
Abstract: Building performance analysis applications have focused on the evaluation
of specific designs based on static, uniform indoor environments. In reality, people
live in a dynamic environment, neither indoor environments nor building occupants
are static, and that would make thermal sensation experienced by an occupant in a
building unstable and challenging to evaluate through the time. The cur-rent field
survey methodology to evaluate thermal comfort in buildings according to
Performance Measurement Protocols for Commercial Buildings (PMPCB) is based
on instrumental measurement of indoor climate and questionnaires to be answered
by building occupants in a specific space at the exact time. Some studies have
questioned this approach due to the inconsistency of physical measurement,
sampling procedures, and doubtful estimations of some other variables. These are
likely to contribute to the incredibility of the survey and possibly affect the overall
prediction accuracy.
Nowadays, the advancement of IoT technology has the potential to transform
human-building interaction and improve building energy performance. It has been
estimated that the connected IoT devices are around 9 billion worldwide, and this
number expected to grow to reach 50 billion by 2020. In the built environment, the
ability to control building indoor environmental variables can have a substantial
impact on improving indoor environmental quality and reducing energy
consumption, such control mostly achieved by using sensor technology.
Thus, this paper presents a unique approach to measure real-time human thermal
comfort in the indoor environment. The proposed approach can predict occupants’
thermal satisfaction level of an indoor environment throughout the building’s
operation. The implementation of environmental sensors and a pilot run to evaluate
thermal satisfaction in real-time has been tested. The thermal model in ASHRAE
standard 55 has used to evaluate thermal comfort.
Keywords: Human thermal comfort, Smart buildings, Internet of things (IoT),
Predicted mean vote (PMV), Built environment, Climate change, Building energy
Nowadays, climate change is the biggest threat to human civilization, and it is happening
as a result of human activity. Since the industrial revolution, the increase in Greenhouse
Gas Emissions (GHGs) has led to a rise in global temperatures. The primary source of
GHGs is from burning fossil fuel-based energy. Reducing the amount of energy required
in our everyday life can significantly cut down human impact on the environment. The
1 PhD student, University of Huddersfield, Huddersfield, UK,
2 Professor, University of Huddersfield, Huddersfield, UK,
3 Lecturer, University of Huddersfield, Huddersfield, UK,
A real-time approach to evaluate occupants' thermal comfort in the indoor environment
478 | Proceedings CIB W78, August 2020 | São Paulo, Brazil
built environment (BE) considered as one of the largest emitters of GHGs and a primary
contributor to climate change (Architecure2030, 2018, DOE, 2010, Asadi et al., 2012).
Globally, buildings account for 40% of global energy consumption and contribute to
more than 30% of CO2 emissions (Costa et al., 2013). This has led to a massive concern
in the research community to conduct numerous studies to improve building energy
performance in the BE, on new buildings, in design and construction of building
envelopes such as thermal insulation (Pan et al., 2012, Joudi et al., 2013), lifecycle
analysis (Asif et al., 2007) and optimization (Lam and Hui, 1996); on renovation of
existing buildings (Chantrelle et al., 2011); and on optimization and control of HVAC
and lighting systems (Mary Reena et al., 2018, Brooks et al., 2015).
The increase of energy demand in the BE connected to building occupants and the
necessity of providing better comfort conditions, thermal comfort, visual comfort,
acoustic and air quality (Pérez-Lombard et al., 2008, Nguyen and Aiello, 2013). Previous
studies have shown the thermal conditioning system is among the major of energy end-
use in the BE; it is responsible for about 50% of total energy consumption especially in
non-domestic buildings (Pérez-Lombard et al., 2008, Chua et al., 2013, Ma et al., 2019).
Another study on the evaluation of indoor environmental quality (IEQ) has shown
thermal comfort satisfaction is highly important by building occupants and has a
substantial impact on energy efficiency compared to other comfort needs (Frontczak and
Wargocki, 2011). Accordingly, understanding thermal comfort implications on energy
efficiency in buildings is essential not only to save energy and cut down energy bills, but
it has a vital factor to mitigate human impact on global warming. As a result, several
assessments and rating programs worldwide formed to promote sustainability and green
buildings such as BREEAM, LEED and ENERGY STAR and becomes prevalent in the BE.
Furthermore, architects and engineers use BIM and energy simulation tools to predict
and improve building performance during the design stage of a building's lifecycle.
Nonetheless, during building operations, where buildings consume up to 84% of total
energy use in its lifecycle (DOE, 2010, Becerik-Gerber et al., 2011), buildings experience
several unexpected factors that affect energy performance and occupants' thermal
satisfaction such as sophisticated use of electrical equipment or occupants behavior.
Measuring thermal comfort in operational buildings is quite complicated and requires
an in-depth perception of the environmental factors affects occupancy thermal
satisfaction and cause overuse of energy. Most of the researchers have used the PMV
index to evaluate thermal comfort in the indoor environment. This model also adopted
by most of the standard and energy simulation tools. PMV index takes six parameters.
Namely temperature, humidity air velocity means radiant temperature, metabolic rate
clothing insulation. The knowledge of existing standards to evaluate IEQ is an example
of this complexity. Accordingly, most works in this area carried out a qualitative analysis
based on people performing some activity and answering a questionnaire.
Therefore, this paper aims to present an innovative approach to measure occupants’
thermal comfort in the indoor environment and predict energy-use in real-time. The
wireless sensor technology used to obtain environmental information, including Ambient
temperature, relative humidity and air velocity at every single zone of the indoor
environment. The proposed approach does not affect room layout or occupant’s activity
in the space.
1.1 Thermal comfort in the built environment
Thermal comfort is an essential factor in building design and in operating of indoor
environment and its effect directly to occupants’ satisfaction and energy consumption.
Mustafa Al-Adhami, Song Wu and Elham Delzendeh
479 | Proceedings CIB W78, August 2020 | São Paulo, Brazil
Moreover, the energy used to provide thermal satisfaction is high; around 70% of
primary energy use in commercial buildings goes for heating and cooling systems.
The reason for this poor performance related to several factors in the design and
operation of the building. Such as building envelope design, the efficiency of the
mechanical system and controls. Besides, there is a limitation or weak undressing among
designers and building operators about the range of factors that are possibility affect the
indoor thermal condition. Understand thermal comfort condition can create an excellent
opportunity to save energy while maintain or even improve the level of occupant
Introducing such approach can give a clear understanding of how human response to
the indoor climate variables. The current thermal comfort prediction tool can support the
designer and building operators to understand human thermal comfort. However, these
tools are static and based on manual measurement/input depends on designer/operators
understanding of indoor climate variables.
The main objective of this paper is to present a novel approach to evaluate thermal
comfort according to ASHRAE standard 55 in a single zone. Different from other
thermal comfort tools, the main feature of this approach is to evaluate occupancy
satisfaction and predict energy-use in real-time. The implementation of this approach
can benefit architect, engineer and building operators and better understand thermal
1.2 Sensor technology
Nowadays, the advancement of IoT technology has the potential to transform the BE and
the way building occupants interact with their surroundings. It has been estimated that
the number of connected IoT devices are about 9 billion worldwide, and this estimate is
expecting to grow to reach more than 50 billion in the next few years (Gubbi et al., 2013).
In the BE, the ability to control environmental variables can have a substantial impact on
improving the indoor environment and reducing energy consumption, this method of
control often performed by using sensor technology (Dong et al., 2019). Several sensors
are being used in the BE, whether to understand occupants behavior pattern or to study
the characteristic of the indoor environment. In building operation, these sensors can be
categorized into three types; a) occupancy sensors to collect data from building users
such as Passive Infrared Sensor (PIR) and Ultrasonic sensor, b) Sensors that can be used
to collect data from the environment such as temperature, humidity and CO2, c)
Personal IoT sensors such as wearable sensors, heart rate, etc. A smart sensing system
for thermal comfort can be classified into two classes, 1) human centre studies and 2)
environmental measurement.
In the area of in human-centric design where age, gender and body mass are
considered to determine individual thermal comfort, ambient temperature sensor and
wearable are used. One of the biggest challenges in these studies is that individuals'
thermal sensation is varied among occupants, and there is no fixed point in which all
occupants feel comfort (Abdallah et al., 2016, Liu et al., 2013, Linhart and Scartezzini,
2011, Corgnati et al., 2008). To this end, Yun and Won (Yun and Won, 2012) introduce a
personal comfort system to measure individual thermal comfort and save energy using
developed with the integration of temperature, humidity, and air velocity sensors, the
char works as a macro-zone controller. (Sardini and Serpelloni, 2010) uses a wireless
sensor network (WSN) to determine the indoor temperature. The sensor attached to the
electromechanical generator, which is powered by the indoor air velocity.
A real-time approach to evaluate occupants' thermal comfort in the indoor environment
480 | Proceedings CIB W78, August 2020 | São Paulo, Brazil
This research is looking onto design a real-time approach enables researchers,
building's operator to collect environmental parameter and occupants' thermal
satisfaction of a single zone in real-time for better decision making and avoid
unnecessary energy consumption.
Usually, building performance analysis applications have focused on the evaluation of
specific designs based on static, uniform indoor environments. People live in a dynamic
environment, neither indoor environments nor building occupants are static or uniform,
and that would make thermal sensation experienced by an occupant in a building
unstable, complicated and nearly impossible to evaluate. Moreover, thermal comfort-
energy conservation requires an advance understanding of the occupant's comfort level
in the indoor environment.
This study has established a new approach of thermal comfort-energy sensing using
a developed IoT sensor and data analytics technique using a machine learning (ML)
regression model to predict the energy-consumption in real-time. This approach can help
the building's operator to evaluate occupant's thermal satisfaction and make the building
more energy efficiency. Although many design measurements considered building
energy performance factors, finding the right balance between energy performance and
occupancy thermal satisfaction in the operation of buildings is absolutely challenging.
The current thermal comfort model in the international standards is used for evaluation
of the indoor environment for a short period. Furthermore, there is a weak
understanding of the relationship between occupants' thermal comfort and the amount of
energy consumption related to them.
The thermal comfort-energy evaluation approach proposed in Erro! Fonte de
referência não encontrada.’ contains two modules. The first module is the evaluation of
occupants' thermal comfort, which includes the characteristics of the indoor
environment associated with thermal comfort returned by the developed IoT sensors.
The data collected from the sensors are being used in the thermal comfort model in
ASHRAE 55 standard to predict the level of occupant's satisfaction. A function has been
developed to calculate PMV values in real-time based on the tool published by CBE
University of California (Schiavon et al., 2014). However, this study has only considered
the environmental parameters for the evaluation of thermal comfort, the personal factors
are established based on the function of the space to set the level of activity, and the time
of the year to set the type of clothing insulation.
The second module is the energy prediction. The prediction module consists of data
generation and data machine learning. In order to predict the energy performance of a
single zone in the building, an energy simulation tool has been used to generate
synthetic data for that zone, considering all the possibilities of energy-use. in the
simulation two types of data used as input static and parametric. Static data include 1)
the energy model of the building considering all the properties of building components
construction and the type of opening; 2) weather data information.
Parametric data include operation schedules for heating and cooling, occupancy
schedule, and humidity control. This study has focused on the source of energy from the
heating and cooling system in the building. Thus, any source of energy not related to the
thermal conditioning system in the building has been disabled in the execution of the
simulation, such as lightings, computers, and office equipment.
Mustafa Al-Adhami, Song Wu and Elham Delzendeh
481 | Proceedings CIB W78, August 2020 | São Paulo, Brazil
Figure 1: Energy-thermal comfort evaluation approach
There are several tool and applications used to measure thermal comfort, such as climate
consultant, Ecotect weather tool, Designbuilder, AHERAE thermal comfort too(Hudson
and Velsaco, 2018). These applications need weather files which include one-year
historical data. The main feature of these tools is to help read and understand weather
data and show a summary of the selected weather file. It enables users to suggest
strategies and techniques for better energy efficiency building, each of which based on
its climate. In general, it centres on climate analysis rather than human thermal comfort,
Moreover, it does not reflect the latest standard (Schiavon et al., 2014).
To this end, there is a need to developing a thermal comfort sensor that can aid
building users and operators to understand occupants' thermal behaviour and assist them
in applying the right strategies to minimize energy end-use. Thus, this study has
proposed a real-time thermal comfort evaluation approach to predict energy
consumption in the indoor environment.
3.1 System architecture
The proposed system can be divided into three layers: a) data acquisition using IoT
sensors (Physical); b) data storage and data processing (back-end software), and c) data
visualization (front-end software) see Erro! Fonte de referência não encontrada.’.
A real-time approach to evaluate occupants' thermal comfort in the indoor environment
482 | Proceedings CIB W78, August 2020 | São Paulo, Brazil
Figure 2: System overview
The physical layer includes environmental sensors to measure attributes from the
indoor environment. The implementation consists of commercially available sensors to
capture human thermal comfort environmental-related data through a Wi-Fi module that
provides two-way data transmission, sent and receive. The sensors used in this study
include temperate, humidity, air velocity, and Wi-Fi module (see Figure 3). All
environmental sensors are powered by five voltage from a power bank using a standard
Universal Serial Bus (USB) cable and connected to the internet using the Wi-Fi module.
Figure 3: Thermal comfort sensor
The back-end software includes client read values from physical sensors over Wi-Fi
and transmit it every 30 seconds. The client sends data from sensors and stores them in
the cloud database. The data is stored in a separate table for each type of sensor in the
cloud. This study is also using web page programming languages, HTML, JavaScript, and
jQuery to control the data. Furthermore, this study has developed a thermal comfort
model based adopted from CBE’s comfort calculator following ASHRAE standard 55.
The developed model receives environmental values from sensors temperature, humidity,
and air velocity and evaluates occupants' thermal satisfaction. In the developed thermal
comfort model personal values metabolic rate and clothing level are fixed according to
the general activity in the space and the season of the year see Erro! Fonte de referência
não encontrada..
The front-end software is using data stored in the cloud database for representation
and user interaction. A flexible visualization technique is required to accommodate
sensors data. There are several visualization techniques presented by previous studies
that can be utilized for representation. An immersive visualization technique is still
under development. The proposed thermal comfort evaluation system shows one of the
IoT applications. It enables building users to observe the level of occupants’ satisfaction
in space in real-time.
Mustafa Al-Adhami, Song Wu and Elham Delzendeh
483 | Proceedings CIB W78, August 2020 | São Paulo, Brazil
Table 1: Example of clothing insulation and metabolic rate values
rate (met)
Clothing level
values (clo)
Reading seated
Typica summer indoor
Trouser, long sleeve shirt
Standing relax
Jacket, Trouser, long sleeve shirt
Typica winter indoor
3.2 Predicted Mead Vote in thermal comfort calculator
The proposed thermal comfort calculated in this study is based on the classic steady-
state model for air-condition spaces proposed by Fanger, Predicted Mean Vote (PMV)
index model (Fanger, 1970). The PMV model aims to predict the thermal sensation of
occupancy in mechanical ventilated space. Fanger’s model measured using four
environment factors and two personal factors. The environmental-related factors are
temperature, mean radiant temperature, air velocity, and humidity. The personals factors
are metabolism and clothing. The calculation of the PMV values, as follows (Fanger,
       (1)
           
   
) (2)
         
 
)} (3)
Where M: metabolic rate (W/m2)
W: external work (W/m2) (assumed to be 0),
: clothing insulation
: clothing factor, ta: air temperature (°C)
: mean radiant temperature (°C),
v: air velocity (m/s)
: vapor pressure of air (kPa)
: convective heat transfer coefficient (W/(m2K))
: surface temperature of clothing (°C)
A real-time approach to evaluate occupants' thermal comfort in the indoor environment
484 | Proceedings CIB W78, August 2020 | São Paulo, Brazil
e: Euler’s number (2.718)
The PMV model has a seven-point scale see Erro! Fonte de referência não encontrada.;
it's recommended that the PMV value should lie within -0.5 to +0.5 to ensure the best
thermal comfort by most occupants.
Table 2: PMV thermal sensation scale
Slightly warm
Slightly cool
3.3 Energy prediction
The energy prediction in this study based on the generation and ML training of multiple
synthetic data of a specific space in the building. The Design builder has been used to
generate an hourly prediction of PMV index, Indoor Air Temperature, indoor Relative
Humidity, and energy consumption for one year. An ML regression model using the
decision forest algorithm presented by (Criminisi et al., 2012) is used to train the
generated synthetic data of the energy simulation. Then, the trained data from the
regression model was used to predict the energy consumption of the indoor environment
based on the collected environmental attributes from the distributed IoT thermal comfort
sensors (see Figure 4) the overview of the energy prediction workflow.
Figure 4: Overview of energy prediction workflow
The proposed approach implemented in a postgraduate (PGR) office environment at a
University of Huddersfield, UK, where occupants are acting naturally without any
Mustafa Al-Adhami, Song Wu and Elham Delzendeh
485 | Proceedings CIB W78, August 2020 | São Paulo, Brazil
interference by the researcher. The office is about 240 m2. The main activity is
stationary office work. The ventilation system includes a heat recovery unit (HRU).
Several CFD simulations have been performed to understand the temperature
distribution of the space in the peak climate conditions in winter and summer. The heat
map of the temperature distribution in the studied indoor environment can support the
decision of placing the thermal comfort sensor see Figure 4. However, in this
experiment only one sensor has been used.
The energy model has been developed for the entire building considering orientation,
construction properties, opening, and HVAC systems in the building. During the
execution of the energy simulation, only PGR office is measured for energy use, air
temperature, radiant temperature, relative Humidity, and Fanger PMV. The
implementation of the decision forest regression model requires training and testing data.
Thus, the data from energy simulation has been divided into two sets 70% used in the
training and 30% for testing. The results from the trained model have displayed the mean
absolute error is 0.676479, and the coefficient of determination is 0.914263. Comparing
the results from prediction and original data from the simulation, it shows some false in
the prediction see Figure 5. This can be fixed by adding more parameter and generating
more dataset for training.
Figure 5: Compare the results from prediction and simulation
In this study, a thermal comfort model sensor has been developed to predicted occupants’
thermal satisfaction within the indoor environment. Three sensors used to collect
environmental parameters, temperature, humidity, and air velocity. A Wi-Fi module to
receive and transmit the sensors data to a cloud database. An online developed thermal
comfort evaluation model is used to calculate PMV values in real-time. The system also
includes ML trained model to predict the amount of energy used for heating and cooling
in the indoor environment.
The output of the presented approach comprises two types of data real and synthetic.
The real data are captured from the indoor environment. These data are being collected
from the developed thermal comfort sensors. The synthetic data generated from energy
simulation include hourly energy performance, indoor climate, and thermal comfort of
the studied environment. The ML decision forest regression algorithm is used to predict
energy use. The experiment has some limitation which can be listed as follows:
Using one thermal comfort sensor is not adequate to accurately estimate thermal
comfort in the studied environment, the CFD simulation has shown multiple
thermal zones need to be included in the study.
A real-time approach to evaluate occupants' thermal comfort in the indoor environment
486 | Proceedings CIB W78, August 2020 | São Paulo, Brazil
The mean absolute error of the trained model is not good enough for an accurate
prediction and it has some false prediction. Hence, it requires more data to
improve the accuracy of the prediction.
The thermal comfort sensor is consuming more energy than expected. The
developed system is transmitting data every 30 seconds, and that would make the
power bank loses its power in less than four days.
Future work will focus on improving the ML prediction model by providing more
synthetic data. Parametric simulation is going to be considered to build an optimization
model to predict the optimum environmental attribute to minimize energy use for
heating and cooling.
ABDALLAH, M., CLEVENGER, C., VU, T. & NGUYEN, A. Sensing occupant comfort
using wearable technologies. Construction Research Congress 2016, 2016. 940-950.
ARCHITECURE2030. 2018. The 2030 Challenge [Online]. Architecure 2030: Architecure Available:
[Accessed 5/10/2018].
ASADI, E., DA SILVA, M. G., ANTUNES, C. H. & DIAS, L. 2012. Multi-objective
optimization for building retrofit strategies: A model and an application. Energy and
Buildings, 44, 81-87.
ASIF, M., MUNEER, T. & KELLEY, R. 2007. Life cycle assessment: A case study of a
dwelling home in Scotland. Building and environment, 42, 1391-1394.
BECERIK-GERBER, B., JAZIZADEH, F., LI, N. & CALIS, G. 2011. Application areas and
data requirements for BIM-enabled facilities management. Journal of construction
engineering and management, 138, 431-442.
efficient control of under-actuated HVAC zones in commercial buildings. Energy and
Buildings, 93, 160-168.
2011. Development of a multicriteria tool for optimizing the renovation of buildings.
Applied Energy, 88, 1386-1394.
CHUA, K., CHOU, S., YANG, W. & YAN, J. 2013. Achieving better energy-efficient air
conditioninga review of technologies and strategies. Applied Energy, 104, 87-104.
CORGNATI, S. P., FABRIZIO, E. & FILIPPI, M. 2008. The impact of indoor thermal
conditions, system controls and building types on the building energy demand.
Energy and buildings, 40, 627-636.
COSTA, A., KEANE, M. M., TORRENS, J. I. & CORRY, E. 2013. Building operation and
energy performance: Monitoring, analysis and optimisation toolkit. Applied Energy,
101, 310-316.
CRIMINISI, A., SHOTTON, J. & KONUKOGLU, E. 2012. Decision forests: A unified
framework for classification, regression, density estimation, manifold learning and
semi-supervised learning. Foundations and Trends® in Computer Graphics and
Vision, 7, 81-227.
DOE 2010. Buildings Energy Data Book. In: OFFICE OF ENERGY EFFICIENCY AND
RENEWABLE ENERGY, U. S. D. O. E. (ed.). Washington, DC, USA (2010): U.S.
Department of Energy.
Mustafa Al-Adhami, Song Wu and Elham Delzendeh
487 | Proceedings CIB W78, August 2020 | São Paulo, Brazil
DONG, B., PRAKASH, V., FENG, F. & O'NEILL, Z. 2019. A review of smart building
sensing system for better indoor environment control. Energy and Buildings, 199, 29-
FANGER, P. O. 1970. Thermal comfort. Analysis and applications in environmental
engineering. Thermal comfort. Analysis and applications in environmental
FRONTCZAK, M. & WARGOCKI, P. 2011. Literature survey on how different factors
influence human comfort in indoor environments. Building and environment, 46,
GUBBI, J., BUYYA, R., MARUSIC, S. & PALANISWAMI, M. 2013. Internet of Things
(IoT): A vision, architectural elements, and future directions. Future generation
computer systems, 29, 1645-1660.
HUDSON, R. & VELSACO, R. 2018. Modelling and Representing Climatic Data in the
Tropics: A Web Based Pilot Project for Colombia. Humanizing Digital Reality.
JOUDI, A., SVEDUNG, H., CEHLIN, M. & RÖNNELID, M. 2013. Reflective coatings for
interior and exterior of buildings and improving thermal performance. Applied
energy, 103, 562-570.
LAM, J. C. & HUI, S. C. 1996. Sensitivity analysis of energy performance of office
buildings. Building and environment, 31, 27-39.
LINHART, F. & SCARTEZZINI, J.-L. 2011. Evening office lightingvisual comfort vs.
energy efficiency vs. performance? Building and Environment, 46, 981-989.
LIU, Y., WANG, L., LIU, J. & DI, Y. 2013. A study of human skin and surface
temperatures in stable and unstable thermal environments. Journal of Thermal
Biology, 38, 440-448.
MA, Z., REN, H. & LIN, W. 2019. A review of Heating, Ventilation and Air Conditioning
technologies and innovations used in solar-powered net zero energy Solar Decathlon
houses. Journal of Cleaner Production, 118158.
MARY REENA, K. E., MATHEW, A. T. & JACOB, L. 2018. A flexible control strategy
for energy and comfort aware HVAC in large buildings. Building and Environment,
145, 330-342.
NGUYEN, T. A. & AIELLO, M. 2013. Energy intelligent buildings based on user activity:
A survey. Energy and buildings, 56, 244-257.
PAN, D., CHAN, M., DENG, S. & LIN, Z. 2012. The effects of external wall insulation
thickness on annual cooling and heating energy uses under different climates.
Applied Energy, 97, 313-318.
PÉREZ-LOMBARD, L., ORTIZ, J. & POUT, C. 2008. A review on buildings energy
consumption information. Energy and buildings, 40, 394-398.
SARDINI, E. & SERPELLONI, M. 2010. Self-powered wireless sensor for air temperature
and velocity measurements with energy harvesting capability. IEEE Transactions on
Instrumentation and Measurement, 60, 1838-1844.
SCHIAVON, S., HOYT, T. & PICCIOLI, A. Web application for thermal comfort
visualization and calculation according to ASHRAE Standard 55. Building
Simulation, 2014. Springer, 321-334.
YUN, J. & WON, K.-H. 2012. Building environment analysis based on temperature and
humidity for smart energy systems. Sensors, 12, 13458-13470.
... For example, the integration of BIM, IoT and rapid laser scanning tools such as LiDAR can aid in visualization and optimized management of the maintenance and CI construction of highways (Esfahan et al., 2017). BIM-IoT-integrated framework has been deployed in case of semi-automated contract management regarding repair and maintenance operations (Li et al., 2020), occupant energy-thermal comfort evaluation (Al-Adhami et al., 2020) and also real-time evaluation of room acoustics using VR (Wyke et al., 2020). ...
Purpose: The purpose of this paper is to investigate BIM integrated IoT architectures extensively and provide comparative evaluation of those against deciding parameters pertaining to their characteristics and subsequent applications in construction industry. Design/methodology/approach: This paper identifies BIM-integrated cyber physical system (CPS) frameworks, specific to project objectives, comprising of sensors working as physical assets and BIM-based virtual models acting as the cyber component , connected via wired or wireless protocols [e.g., WiFi, Zigbee, near-field communication (NFC), mobile-to-mobile (M2M), Zwave, 3G, 4G, LTE, 5G, and low-power wide-area networks (LP-WAN)] and their potential applications in decision-making, visual management, logistics and supply chain management, smart building system management and structural performance assessment etc. Such proposed architectures are evaluated against deciding parameters such as availability, reliability, mobility, performance, management, scalability, interoperability and security & privacy to evaluate their respective efficiencies. Findings: Study finds that the underlying aim of planned IoT frameworks is to integrate systems and processes for a better information flow and to initiate shift from silo solutions to a smart ecosystem. The efficiencies of such frameworks are completely subjective to their respective project natures, objectives and requirements. Originality/value: This study is unique in its nature to identify requirements of an efficient BIM integrated IoT architecture and provide comprehensive insights about potential applications in construction industry.
Full-text available
Understanding local climate is a critical factor in the design of buildings that requires continuous research to widen its scope, deepen base information and seek data correlations. In tropical regions climate varies greatly across relatively small areas, changes in altitude and geography define conditions that add complexity to typical climate patterns. Paucity of data makes understanding tropical climates difficult. Scarcity of design strategies and computational tools designed for the tropics means bio-climatic design and basic low- energy strategies are poorly understood and underutilised. This paper describes the first phase of a collaborative project aimed at addressing these issues in Colombia.
Conference Paper
Full-text available
Thermal comfort of building occupants is a major criterion in evaluating the performance of building systems. It is also a dominant factor in designing and optimizing building’s operation. However, existing thermal comfort models, such as Finger’s model currently adopted by ASHRAE Standard 55, rely on factors that require bulky and expensive equipment to measure. This paper attempts to take a radically different approach towards measuring the thermal comfort of building occupants by leveraging the ever-increasing capacity and capability of mobile and wearable devices. Today’s commercially-off-the-shelf (COST) wearable devices can unobtrusively capture a number of important parameters that may be used to measure thermal comfort of building occupants, including ambient air temperature, relative humidity, skin temperature, perspiration rate, and heart rate. This research evaluates such opportunities by fusing traditional environmental sensing data streams with newly available wearable sensing information. Furthermore, it identifies challenges for using existing wearable devices and to developing new models to predict human thermal comfort. Findings from this exploratory study identify the inaccuracy of sensors in cellphones and wearable as a challenge, yet one which can be improved using customized wearables. The study also suggests there exists a high potential for developing new models to predict human thermal sensation using artificial neural networks and additional factors that can be individually, unobtrusively, and dynamically measured using wearables.
Innovations in Heating, Ventilation, and Air Conditioning (HVAC) systems are continuously required to provide a better, healthier and more productive and sustainable built environment for building occupants with minimized energy or cost consumption. This paper provides an overview of the HVAC technologies and systems used in 212 solar-powered houses developed through 13 U.S. Department of Energy Solar Decathlon (SD) competitions. Some comments and discussions on the HVAC technologies and systems used in the SD competitions were also provided. The review was carried out based on the information available from the organizer’s project reports and equipment summary, team project manuals, and construction drawings available on the SD official websites as well as the published research papers and textbooks. It was found that 84.9% and 89.6% of the competition teams used heat pumps for space heating and space cooling, respectively, among which air-to-air heat pumps were used by approximately 50% of the competition teams. A wide range of energy technologies such as phase change materials, night-time radiative cooling, evaporative cooling, desiccant dehumidification, and energy/heat recovery ventilators have been used to reduce the electricity consumption of the HVAC systems. Energy/heat recovery ventilators were used by more than 55% of the teams in each competition held after 2005. Phase change materials were also frequently used in the competitions held in Europe. The SD competitions provided an excellent platform to showcase innovations of the HVAC technologies in residential buildings.
This paper aims to provide a systemic review of how indoor sensors influence in managing optimal energy saving, thermal comfort, visual comfort, and indoor air quality in the built environment. The optimal management of energy saving and occupant comfort plays a vital role in the built environment because the occupant's productivity and health are highly influenced by indoor environmental quality. In order to do this, there must be a functional sensing system that connects the environment variables (e.g., temperature) with building environmental control systems such as the heating, ventilation, and air-conditioning system. This paper starts with an overview of the importance of energy saving and occupant comfort in the built environment. It then discusses sensors and their importance in the built environment and reviews the different types of sensors, which explains them in terms of how they influence the indoor built environment and occupant productivity. The paper further explores the application of sensors in the built environment and analyzes this in terms of energy saving, thermal comfort, visual comfort, and indoor air quality. Following this, the data analysis is discussed in terms of data, information, and knowledge accrued from the sensors. Lastly, the paper discusses the future challenges for the improvement of building indoor environmental quality and energy saving by the application of sensors.
A data driven framework for the energy and comfort management in large buildings with multiple zones and dynamic occupancy patterns is presented in this paper. For such cases, precise heat conduction models derived using the classical thermal physics laws will be cumbersome. The approach uses the historical data to develop a multi-variable model through Structural Equation Modeling (SEM) so as to identify the relative dominance of the direct and indirect effects of thermal coupling among the neighboring zones, occupancy and the external climate variations on the thermal behavior of the building zones. Based on the information gathered from the SEM, we can predict the return temperatures more accurately, which in turn is employed to incorporate a flexible control strategy for the HVAC system. A controller fed with the temperature error and occupancy error, between the predicted and measured values, regulates the supply air fan speed via VFD motor and outside air damper valve openings. This has resulted in the energy savings while maintaining the occupant thermal comfort at the reasonable levels. The framework proposed has been evaluated using real data collected from an HVAC system of a big airport terminal building. The results show that the accuracy of prediction is relatively higher than with other regression techniques; and that the HVAC system is energy efficient and can ensure occupant comfort on real-time basis in large buildings.
Thermal comfort is one of the fundamental aspects of indoor environmental quality and it is strongly related to occupant satisfaction and energy use in buildings. This paper describes a new web application for thermal comfort visualization and calculation according to ASHRAE Standard 55-2013. Compared to existing software, the web application is free, cross-platform, and provides a visual and highly interactive accurate representation of the comfort zone. Its main features are: dynamic visualization of the comfort zone on psychrometric, temperature-relative humidity, and adaptive charts; new implementation of the Elevated Air Speed model; local thermal discomfort assessment; compliance document automation for LEED thermal comfort credits; metabolic activity and clothing insulation tables and dynamic models; and compliance with the standard. The tool can be used by architects, engineers, building operators, educators, and students.