ISSN 2004-2965

Energy Proceedings, Vol. 24, 2021

International Conference on Applied Energy 2021
Nov. 29 - Dec. 5, 2021, Thailand/Virtual
Paper ID: 390

Pilot Study on Early Warning Systems for Thermal Discomfort Using Body
Surface Temperature for Energy Conservation in the Building Sector
Ziyang Wang1, Ryuji Matsuhashi1*, Hiroshi Onodera1
1 Department of Electrical Engineering and Information Systems, The University of Tokyo, Tokyo, 113-8656, Japan
Email addresses: wang@enesys.t.u-tokyo.ac.jp, matu@enesys.t.u-tokyo.ac.jp, onodera@ee.t.u-tokyo.ac.jp

ABSTRACT
Buildings consume a huge amount of energy andmainly utilize it for occupants' thermal comfort
satisfaction. Real-time thermal comfort assessment canenormously contribute to thermal comfort optimizationand energy conservation in buildings. Existing thermal
comfort models mainly focus on the real-timeassessment of occupants' current thermal comfort.
However, in the transient thermal environment,
occupants' thermal comfort is unsteady and varies fromtime to time. Therefore, if we only assess occupants'
current thermal comfort, prediction error will be elicited.
In order to address this problem, it is principally
important to comprehend occupants' real-time thermal
sensation trend in the transient thermal environment.
This study investigates a novel thermal sensation index
that can directly represent an individual's current
thermal sensation trend. By incorporating the novel
thermal sensation index into an ordinary thermal
comfort model, a composite thermal comfort model isderived, which can simultaneously address anindividuals' current thermal comfort and current thermal
sensation trend. Then, by utilizing a machine learningclassification algorithm, we propose its intrusive
assessment method using skin or clothing temperaturesof ten local body parts measured by thermocouple
thermometers and its non-intrusive assessment method
using a low-cost portable infrared camera. The novelcomposite thermal comfort model can provide an early
warning mechanism for thermal discomfort andcontribute to energy conservation in buildings.
Keywords: thermal comfort, energy conservation,
physiological index, infrared thermography, machinelearning

NOMENCLATURE
Abbreviations
RTS
3-point RTSS
RTSV
ATS
7-point ATSS
ATSV
3-point TCS
TCL
9-point TTCS
TTCL
MLP
LBPT
DRTP

Relative Thermal Sensation
3-point Relative Thermal Sensation
Scale
Relative Thermal Sensation Vote
Absolute Thermal Sensation
7-point Absolute Thermal Sensation
Scale
Absolute Thermal Sensation Vote
7-point Thermal Comfort Scale
Thermal Comfort Level
9-point Transient Thermal Comfort
Scale
Transient Thermal Comfort Level
Multilayer Perceptron
Local body part temperatures
Dimension-reduced thermal profiles

1.

INTRODUCTION
Buildings account directly and indirectly for over onethird of global final energy consumption [1], a large
proportion of which (more than 50%) is used for thermal
comfort maintenance through the Heating, Ventilation,
and Air-conditioning (HVAC) systems. Real-time thermal
comfort assessment is not only essential in constructing
the control module of the HVAC system but also rather
critical in energy conservation in buildings.
Existing thermal comfort models mainly focus on theassessment of occupants' current thermal comfort [2,3].
However, in the transient thermal environment,
occupants' current thermal comfort is not stable andvaries from time to time [4,5]. For instance, in winter,

Selection and peer-review under responsibility of the scientific committee of the 13th Int. Conf. on Applied Energy (ICAE2021).
Copyright © 2021 ICAEISSN 2004-2965

Energy Proceedings, Vol. 24, 2021

suppose an occupant comes back home and turns on theheating system to create a cozy environment. After
heating the house for a while, the occupant may feel
comfortably warm. Meanwhile, if the power of theheating system is too high, even though the occupant's
current thermal comfort is "comfortable," the occupant's
thermal sensation may become warmer and warmer.
Therefore, it is reasonable to infer that the occupant will
likely fall into an uncomfortably hot state if the heating
system continues working. In that case, it would bebetter to lower the power of the heating system inadvance to prevent the occupant from running into an
uncomfortably hot state. If the occupant's thermal
sensation trend can be accurately assessed, not only canthermal discomfort be predicted in advance, but energy
can also be saved.
In order to address this concern, in this paper, we
present a composite thermal comfort model byintroducing a novel thermal sensation index into an
ordinary thermal comfort model.
2.

2.2 Composite thermal comfort model
In this study, to better distinguish from the RTS andthe 3-point RTSS, we denominated the thermal sensationin the common sense as the Absolute Thermal Sensation(ATS). By such a definition, the thermal sensation isdecomposed into two categories: ATS and RTS.
Moreover, we define the 7-point Absolute Thermal
Sensation Scale (7-point ATSS) consists of sevencategories "very cold" (-3), "cold" (-2), "cool" (-1),"neutral" (0), "warm" (+1), "hot" (+2), and "very hot"(+3), each category in which is called an Absolute
Thermal Sensation Vote (ATSV). In the 7-point ATSS, the"cool," "neutral," and "warm" votes are defined torepresent comfortable sensations; the "hot" and "veryhot" votes are defined to represent uncomfortably hot
sensations; the "cold" and "very cold" votes are defined
to represent uncomfortably cold sensations.
Consequently, the 3-point Thermal Comfort Scale (3point TCS) can be derived from the 7-point ATSS. The 3point TCS has three categories "hot" (+3), "cozy" (0), andMETHODOLOGY

2.1 Relative Thermal Sensation
In this study, the thermal sensation is considered to
be a first-order differentiable function f (t ) of time t ,where f (t )  0 denotes hot sensation, f (t )  0
denotes cold sensation, f (t ) = 0 denotes neutral
sensation. The larger its absolute valuef (t ) , thegreater the degree of hotness or coldness. The first-orderderivative of f (t ) , describing the trend of the thermal
sensation, is precisely defined as the Relative Thermal
Sensation (RTS) in this study. Next, the 3-point Relative
Thermal Sensation Scale (3-point RTSS) is defined to bediscrete and consists of three categories "hotter" (+1),"no change" (0), and "colder" (-1), each category in whichis called a Relative Thermal Sensation Vote (RTSV). The
RTSV is defined as "hotter" when the occupant iscurrently feeling hotter than the latest thermal sensation( f (t )  0 ); the RTSV is defined as "colder" when theoccupant is currently feeling colder than the latest
thermal sensation ( f (t )  0 ); when the occupant's
current thermal sensation does not change comparedwith the latest thermal sensation (neither "hotter" nor"colder," when f (t ) = 0 ), the RTSV is defined as "nochange." By such a definition, the RTS forms anotherdimension of thermal comfort, and it can serve as acomplementary thermal sensation index for traditional
thermal comfort models.

Fig. 1 9-point TTCS by incorporating the 3-point RTSS into
the 3-point TCS"cold" (-3), each category in which is called a Thermal
Comfort Level (TCL).
By incorporating the 3-point RTSS into the 3-point
TCS (combine each category in the 3-point RTSS witheach category in the 3-point TCS), we can obtain the 9point Transient Thermal Comfort Scale (9-point TTCS),
which is a composite real-time thermal comfort modeland can describe an individual's current thermal comfort
and current thermal sensation trend simultaneously, as
described in Fig. 1. The 9-point TTCS has nine categories"hot to hot" (+4), "hot" (+3), "hot to cozy" (+2), "cozy tohot" (+1), "cozy" (0), "cozy to cold" (-1), "cold to cozy" (2), "cold" (-3), "cold to cold" (-4), each category in whichis called a Transient Thermal Comfort Level (TTCL).
In the 9-point TTCS, the "hot to hot" and "cold tocold" TTCLs indicate an individual's hot or cold
discomfort degree will aggravate in the future; the "hotto cozy" and "cold to cozy" TTCLs indicate an individual's
hot or cold discomfort degree will alleviate in the future;2

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Energy Proceedings, Vol. 24, 2021

the "cozy to hot" and "cozy to cold" TTCLs indicate anindividual tends to feel uncomfortably hot or cold in the
future.
2.3 Novel thermal sensation voting system
The subjects were requested to input the real-time
ATSVs and RTSVs through a laptop. The number keys "1,""2," "3," "4," "5," "6," and "7" correspond to the ATSVs"very cold," "cold," "cool," "neutral," "warm," "hot," and"very hot," respectively. The arrow keys "up" and "down"correspond to the RTSVs "hotter" and "colder,"respectively, as illustrated in Fig. 2. The subjects should
input either the ATSV or RTSV at least once when hearing
an alarm that rings every 20 sec. The priority of the ATSV
is higher than the RTSV. Moreover, in order to gather as
many thermal sensation details as possible, the subjects
were encouraged to input the ATSV and RTSV wheneverthey felt a change in their thermal sensation. The"hotter" and "colder" RTSVs were regarded to sustain 20

As is known to all, intuitively, people tend to feelhotter when the air temperature is ascending, while theytend to feel colder when the air temperature isdescending. However, the real-time RTS could be much
more complicated than simply judging by the airtemperature. We hypothesized that it is possible toassess the RTS by analyzing the body surface
temperatures of several local body parts or the thermal
images of the body surface. We measured the local bodypart temperatures (LBPT) of ten locations, including theforehead, the upper chest, the lateral arm, the dorsumof the hand, the abdomen, the scapular blade, theanterior thigh, the fibular shin, the posterior wrist, andthe dorsum of the foot in an intrusive way using
thermocouple thermometers (Accuracy: ±0.5℃). Also,
we utilized a low-cost portable infrared camera FLIR ONE
Pro ($375) to cover the subjects' upper body surface
infrared radiation in a non-intrusive manner. Both the
LBPT and the thermal images were taken per sec.
2.5 Feature extraction and feature engineering for RTS
and TCL assessments by classification algorithm

Fig. 2 Keyboard layout for ATSV and RTSV input

sec after the input when there is no successive "hotter"or "colder" RTSV within 20 sec. The "no change" RTSV
was not set as an active vote to alleviate the mental
burden. Time periods with no "hotter" or "colder" RTS
were regarded as the "no change" RTS periods.
2.4 Intrusive and non-intrusive
temperature measuring methods

bodysurface

We used an instance segmentation algorithm(YOLACT++ [6]) to remove the background surrounding
the subjects' thermal profiles in the thermal images andapplied dimensionality reduction to the backgroundremoved thermal profiles using the UMAP [7] to perform
feature extraction and obtain the dimension-reduced
thermal profiles (DRTP) (from 160×120 to 10). Then, weused the LBPT and the DRTP for personal TCL modeling,
separately. Since the RTS is the gradient of the ATS,reasonably, we used the LBPT and the gradients of LBPT
as well as the DRTP and the gradients of DRTP for
personal RTS modeling, separately. Five-fold blocked
cross-validation was carried out to predict personal RTS
and TCL using the Multilayer Perceptron (MLP) classifier
since the data have time dependencies according to [8].
Optimal hyper-parameters were obtained using the gridFig. 3 Overview of the proposed individual TTCL assessment framework for an occupant-centered HVAC control system.

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Energy Proceedings, Vol. 24, 2021

search technique. Fig. 3 illustrates the TTCL assessment
framework using the above-mentioned intrusive andnon-intrusive methods for an occupant-centered HVAC
control system.
3. EXPERIMENTAL SETUP
The experiment was conducted in the environmental
test lab of Tokyo Gas Co., Ltd, which consists of an
external environment and an inner chamber, as
illustrated in Fig. 4. Six male students (age 25 ± 1 years)
at the University of Tokyo participated in the experiment.
By various operations of the HVAC system and the door,
two Scenarios were created to test the RTS and ATS
performance under such thermal conditions, as
illustrated in Fig. 6. Three subjects were randomly
selected and dispatched to Scenario I, and the otherthree were dispatched to Scenario II.

not to directly face the airflow from the air-conditionerof the door, as illustrated in Fig. 4. The subjects wererequested to wear the same clothes provided by us(black short sleeves, trousers, and cotton socks), sit downon the chair, and press the keyboard buttons to input the
ATSV and RTSV during the experiment to simulatesedentary office activities (small range of motion
allowed), as shown in Fig. 5.
4. RESULTS AND DISCUSSION
4.1 Visualization of the LBPT, DRTP, RTSVs and ATSVs
Fig. 7 and Fig. 8 show the representative data of two
subjects from Scenarios I and II, respectively. In both Fig.
7 and Fig. 8, during "hotter" periods, the shin

Fig. 4 Schematic of the environmental test lab.
Fig. 7 Shin, abdomen, and air temperatures, original
ATSVs and RTSVs, first two UMAP components, andsmoothed first two UMAP components (Scenario I &subject 1).

Fig. 5 A male subject and the mounting state of theexperimental apparatus.

A laptop for ATSV and RTSV input was set on a deskin front of a chair. The desk and the chair were arranged

Fig. 8 Shin, abdomen, and air temperatures, original
ATSVs and RTSVs, first two UMAP components, andsmoothed first two UMAP components (Scenario II &subject 4).
Fig. 6 Temperature control schemes of the inner chamber.

temperature is higher than the abdomen temperature,4

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Energy Proceedings, Vol. 24, 2021

whereas during "colder" periods, the shin temperature islower than the abdomen temperature, indicating that it
could be reasonable to use the LBPT for RTS modeling.
During "hotter" periods, the shin and abdomen
temperatures tend to be ascending, whereas during"colder" periods, they tend to be descending, indicatingthat it could be reasonable to use the gradients of the
LBPT for RTS modeling. For the DRTP, in Fig. 8, during"hotter" periods, the smoothed first two UMAP

components occupy lower ranges; during "colder"periods, they occupy higher ranges. In Fig. 7, during"hotter" periods, the second smoothed UMAP
component occupies a higher range and occupies a lower
range during "colder" periods, indicating that it could bereasonable to use the DRTP for RTS modeling. However,different from the LBPT, the first smoothed UMAP
component in Fig. 7 does not satisfy this pattern since
the UMAP is a non-linear process.

Fig. 9 Prediction of the RTS, TCL, and TTCL (Scenario I & subject 1)

Fig. 10 Prediction of the RTS, TCL, and TTCL (Scenario II & subject 4)5

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Energy Proceedings, Vol. 24, 2021

4.2 Thermal discomfort early warning mechanism of the
TTCL
Table 1. Mean accuracy of the TTCL predictions in
Scenarios I and II using LBPT feature sets or DRTP feature
sets.
Scenario
LBPT
DRTP
TTCL
I
0.57
0.45
II
0.51
0.41

Table 1 shows the mean accuracy of the TTCL
predictions in Scenarios I and II integrated by the RTS
predictions and the TCL predictions using LBPT featuresets or DRTP feature sets. The LBPT feature set reachedmean accuracies of 0.57 and 0.51 in Scenarios I and II,
respectively; the DRTP feature set reached mean
accuracies of 0.45 and 0.41 in Scenarios I and II,
respectively. Since the number of classes is relatively
large (nine), the prediction result is not bad.
Fig. 9 and Fig. 10 show the RTS predictions, TCL
predictions, and the integrated TTCL predictions using
the LBPT or DRTP feature sets of two subjects from
Scenarios I and II, respectively. For instance, in Fig. 9, inthe first gradual heating phase, during 400-550 sec, thesubject's TCL was still "cozy" while feeling "hotter," theTTCL was mainly "cozy to hot" and was successfully
predicted using the LBPT feature set; in Fig. 10, in thegradual cooling phase during 1700-1850 sec, thesubject's TCL was still "cozy" after turning on the coolingmode of the air-conditioner and finally reached "cold,"
the TTCL was mainly "cozy to cold" and was successfully
predicted using both LBPT and DRTP feature sets. The
significance of the TTCL is that "cozy to hot" and "cozy tocold" TTCLs can generate thermal discomfort early
warning mechanisms. In such cases, it is better to turndown the heater/chiller in advance to prevent it fromrunning into "hot" or "cold" TCLs. Not only can thermal
comfort be maintained, but energy can also be saved.
Moreover, the significance of the TTCL is that "hot tohot" and "cold to cold" TTCLs can generate thermal
discomfort deterioration prevention mechanisms thatcan prevent occupants' current thermal comfort fromdeteriorating. For instance, in Fig. 9, in the first gradual
heating phase during 1600-1800 sec, the subject's TTCL
was mainly "hot to hot" and was successfully predictedusing the LBPT feature set; in Fig. 9, in the gradual
heating phase during 2150-2250 sec, the subject's TTCL
was mainly "cold to cold" and was successfully predictedusing both LBPT and DRTP feature sets.
Furthermore, our RTS models have the ability to
predict some fine details of the RTS, indicating thevalidity of the RTS models. For example, in Fig. 9, during
1780-2050 sec, the RTS fine details were well predictedby the DRTP feature set; in Fig. 10, during 850-950 sec,
the fine details of the RTS were well predicted by the
LBPT feature set.
5. CONCLUSION
This work investigates a novel composite thermal
comfort model that simultaneously addresses theoccupants' current thermal comfort and the current
thermal sensation trend. This pilot study facilitatespractical applications for thermal discomfort early
warning/deterioration
prevention
systems
andcontributes to energy conservation in buildings.
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