# Cohort comfort models — Using occupants’ similarity to predict personal
thermal preference with less data

Matias Quintana 1, Stefano Schiavon 2, Federico Tartarini 2, Joyce Kim 3,
Clayton Miller1,∗ 1College of Design and Engineering, National University of
Singapore (NUS), Singapore  2Center for the Built Environment, University of
California Berkeley, U.S.  3Department of Civil and Environmental Engineering,
University of Waterloo, Canada  ∗Corresponding Author<EMAIL_ADDRESS>+65 81602452

###### Abstract

Cohort Comfort Models (CCM) are introduced as a technique for creating a
personalized thermal prediction for a new building occupant without the need
to collect large amounts of individual comfort-related data. This approach
leverages historical data collected from a sample population, who have some
underlying preference similarity to the new occupant. The method uses
background information such as physical and demographic characteristics and
one-time onboarding surveys (satisfaction with life scale, highly sensitive
person scale, personality traits) from the new occupant, as well as
physiological and environmental sensor measurements paired with a few thermal
preference responses. The framework was implemented using two personal comfort
datasets containing longitudinal data from 55 people. The datasets comprise
more than 6,000 unique right-here-right-now thermal comfort surveys. The
results show that a CCM that uses only the one-time onboarding survey
information of an individual occupant has generally as good or better
performance as compared to conventional general-purpose models, but uses no
historical longitudinal data as compared to personalized models. If up to ten
historical personal preference data points are used, CCM increased the thermal
preference prediction by 8% on average and up to 36% for half of the occupants
in the first of the tested datasets. In the second dataset, one-third of the
occupants increased their thermal preference prediction by 5% on average and
up to 46%. CCM can be an important step toward the development of personalized
thermal comfort models without the need to collect a large number of
datapoints per person.

###### keywords:

Thermal comfort , Clustering , Personalized environments , Cold start , Warm
start , Recommender systems

††journal: Building and Environment

## Nomenclature

PMV

    Predicted Mean Vote
PCM

    Personal Comfort Model
PCS

    Personal Comfort System
CCM

    Cohort Comfort Model
RHRH

    Right-Here-Right-Now
HSPS

    Highly Sensitive Person Scale
SWLS

    Satisfaction With Life Scale
RF

    Random Forest
JS

    Jensen-Shannon
RBF

    Radial Basis Function
B5P

    Big Five Personality

##  1 Introduction

In the built environment, indoor thermal comfort conditions may influence
health [1, 2], office work performance [3, 4, 5, 6], learning performance [7,
8, 9], well-being [10, 11], and the overall satisfaction of occupants [12, 6,
13]. An analysis done by Graham et al. (2021) based on approximately 90,000
occupant satisfaction survey responses found that roughly 40% of them are
dissatisfied with their thermal environment [14].

The Predicted Mean Vote (PMV) [15] thermal comfort index is widely used in
industry and research. The PMV model aims to predict the average thermal
sensation of a group of people sharing the same thermal environment. Although
Fanger [15] was aware that people have different thermal sensations, he
developed the PMV model since many buildings are not designed to provide
personalized cooling and heating to individual occupants. Hence, the PMV aims
to find the optimal indoor conditions to ensure that most occupants are
comfortable. Another popular thermal comfort model included in the ASHRAE55 is
the Adaptive model [16]. This model is based on the prevailing mean outside
air temperature and only applies to naturally conditioned buildings. Both
models consider environmental and personal conditions affecting the occupant
rather than specific temperature set-points as fixed comfort thresholds, but
they average the individual occupants’ responses. A study on the ASHRAE Global
Thermal Comfort Database II [17] provides evidence of the low accuracy of the
PMV model, i.e., 33% [18] in predicting the thermal sensation of individual
occupants. This, combined with the proliferation of Internet-of-Things (IoT)
sensors and wearable devices, has pushed researchers to look into alternative
solutions to predict how occupants perceive their thermal environment. Data-
driven approaches leverage direct feedback from occupants paired with
environmental and physiological data to develop personalized thermal comfort
models [19]. These models can then be used to optimize the operation of
Heating, Ventilation, and Air-conditioning (HVAC) systems or to quantify the
thermal environment’s quality in buildings. With Personal Comfort Model [19]
researchers can use environmental, physiological, and behavioral data to
predict thermal sensation or preference. To investigate relationships between
the variables mentioned above, field experiments offer a more realistic
context of a person’s experiences compared to most climate chamber studies
[20]. Collecting field data poses several challenges since people must
complete surveys while performing day-to-day tasks. This may disrupt their
activities, and accurate monitoring and logging of environmental variables
with a high spatial and temporal resolution are expensive and nontrivial.
Moreover, even when there is enough available data to develop a PCM for all
occupants, it is still problematic from the control strategy perspective to
ensure that all occupants will find the thermal environment comfortable [21].
Another common approach is to develop a general-purpose model to make the most
of the available data. This model is trained on the whole population of
occupants so that the available data for training is maximized at the expense
of personalization. In open shared spaces where the HVAC system cannot be
designed to tailor to individual needs, one option is to control environmental
parameters to maximize the number of comfortable occupants and, at the same
time, provide Personal Comfort Systems (PCS). Each occupant can use the PCS
(e.g., heated and cooled chairs, desk fans, and foot warmers) to adjust the
environment based on their needs and preferences.

In this paper, we seek to investigate whether we can leverage historical
information gathered from building occupants to predict the thermal comfort
preferences of a new person from which little individual data is available. By
individual data, we refer to longitudinal subjective responses to Right-Here-
Right-Now (RHRN) surveys about thermal preference. In particular, our research
hypothesis is that the thermal comfort preferences of building occupants can
be estimated by analyzing their psychological and behavioral traits and
comparing them to other occupants from which subjective feedback was
previously obtained. This way, we leverage the personalization aspect of PCM
with less data.

In previous studies, the principal value of subjective responses alone was
used to group occupants (i.e., the proportion of subjective feedback that is
prefer cooler, no change, or prefer warmer) [22] whereas in [23] a comfort
profile was first determined based on some historical data. The new occupants
were compared against the available group profiles. In both works, once
similarities were found between occupants, the group’s data was used for the
prediction model for new occupants. Environmental measurements (e.g., air
temperature and relative humidity) coupled with physiological measurements
(e.g., height and weight) were used as features in the model [24]. However, in
both approaches, some data is still required from the new occupant to
correctly assign it to a respective group.

In this work we introduce Cohort Comfort Models (CCMs). This framework uses
similarities between occupants to predict how new occupants with little or no
historical data would perceive their environment. More details are explained
in Section 2. With this approach we tried to answer the following questions:

  1. 1.

Do Cohort Comfort Model (CCM) increase the prediction performance for new
occupants compared to a data-driven general-purpose model, i.e., all data are
merged to develop a single comfort model?

  2. 2.

Can we pinpoint which occupants benefited from using CCM, and if so, which
features mainly contributed to improving the prediction accuracy?

##  2 Cohort Comfort Models

###  2.1 Definition

Cohort Comfort Models (CCMs) is a new framework to predict the thermal
preference responses of a new person. CCM builds on Personal Comfort Models
(PCMs) and leverages historical data collected from a sample population, who
have some underlying preference similarity with the new person. By doing this,
CCM leverage the power of both aggregate models and Personal Comfort Models.
The key characteristic of a CCMs is that it is trained on a group of occupants
who share features and subjective preferences with the new person we are
trying to predict. The comparison of data-driven thermal comfort modeling
alternatives against more established ones like Predicted Mean Vote (PMV) and
the adaptive model is shown in Figure 1(a). An overview of the proposed
framework is shown in Figure 1(b).

(a) Diagram of thermal comfort modeling alternatives. The darker blue region
highlights thermal comfort models at the population level (i.e., Predicted
Mean Vote (PMV) and adaptive). The light blue area comprises personalized
thermal comfort models trained on the person itself ( Personal Comfort Model
(PCM)) or on a group of similar people ( Cohort Comfort Model (CCM)).

(b) Cohort comfort modeling framework, built on Personal Comfort Models
(PCMs), leverages historical data collected from a sample population, who have
some underlying preference similarity, to predict thermal preference responses
of new occupants. Firstly the comfort cohorts are created as shown in the top
segment following a one-time data collection effort. Comfort cohorts are
determined using data from the onboarding surveys, sensor measurements, and
subjective thermal comfort data. The middle dark gray segment shows a new
occupant from which only the onboarding survey is acquired and is used to
assign the occupant to one of the previously created cohorts; this is also
known as “cold start”. Finally, the bottom segment shows another new occupant
from which $m$ data points are collected in order to be assigned to an
existing cohort with the possibility of using these $m$ data points to fine-
tune the existing cohort model; this is also known as “warm start” when
$n>>m$. For both cold and warm start, the model corresponding to the assigned
cohort is used to predict the occupant’s thermal comfort with the addition
that the latter can use the already collected information from the occupant to
fine-tune the cohort model.

Figure 1: Comparison of current thermal comfort modeling alternatives (1(a))
and overview of our work  Cohort Comfort Model (CCM) (1(b)).

One of the main contributions of this work is to determine ways in which
characteristics of occupants, relevant to thermal comfort, can be compared.
Firstly, a longitudinal data collection experiment of various buildings
occupants is required, since a high number of participants allows the
framework to find both occupants with similar and different characteristics.
As for the required number of labeled data points per occupant ($n$ in Figure
1(b)), previous relevant works suggests that 50 [25], 60 [19], 90 [26], and
200 [27] data points per occupant are needed for acceptable accuracy and
stable prediction. However, the current landscape on thermal comfort
longitudinal field data collection experiments is very limited in terms of
datasets [28] and only a handful of them contain a high number of data points
($n$) per participant. It should also be noted that the number of data points
needed for each participant varies as a function of the range of environmental
conditions a participant may experience. While a high $n$ would be ideal, our
proposed framework is not restricted to a threshold of available data. Our
framework then utilizes the different data streams from the dataset collected
(top gray segment, Figure 1(b)): onboarding survey, sensor data, Right-Here-
Right-Now (RHRH). Onboarding survey refers to a one-time background survey
that each occupant fills out only once before the data collection experiment
starts. This survey must gather personal data (e.g., sex, height, weight) and
preferably should also include psychological tests, such as the Big Five
personality test [29]. This test is also used for occupant similarity, in the
healthcare monitoring domain, for well-being label prediction [30]. Sensor
Measurements encompasses all environmental and physiological data collected
when the participant completed the RHRH. Subjective Thermal Comfort comprises
the questions asked in the RHRH survey. These three main data streams are used
to define the CCM.These CCMs are groups or clusters capable of reflecting
preference patterns extrapolated from the relationship between Subjective
Thermal Comfort and Sensor Measurements, which are longitudinal time-series
data we will refer to as warm start. Then, CCM obtained from Subjective
Thermal Comfort andonboarding Survey responses will be refered to as cold
start. The naming convention is the same used in the field of Recommender
Systems. A cold start approach means starting off from scratch with no
previous “warm up”. In our case this means we start without asking the
participant to complete any right-here-right-now surveys. This is a harder
start. On the other hand, a warm start scenario assumes some initial
initialization or “warm up” has started, thus making the subsequent efforts
easier. In our case, this means we started with historical longitudinal data.

Once the cohorts are determined and existing occupants are allocated to one,
new occupants can also be assigned to a cohort. The cohort assignment is
coupled with how the cohort was created and will vary depending on the data
availability for the new occupant. When a new occupant has not been through a
longitudinal data collection phase, i.e., only onboarding survey data is
available, they can be assigned to a cohort using a “cold start” assignment
procedure (middle dark gray section in Figure 1(b)). On the other hand, when a
new occupant has been through a longitudinal data collection phase and a
subset of RHRH data points is available (bottom light gray section in 1(b)),
the assignment to a cohort can be done using a “warm start” approach. It
should be noted, that in the latter case, the new occupant can still be
assigned to its respective cohort without leveraging the collected labeled
data points using a “cold start” assignment procedure with the onboarding
survey responses. In addition, the availability of the RHRH data for this
occupant makes it possible to fine-tune the respective cohort model for the
new occupant if required. The details on cold start and warm start cohort
creation and assignations are further explained in Section 2.3.

###  2.2 Current Related Work

####  2.2.1 Data-driven thermal comfort prediction

Data-driven thermal comfort models rely on a handful of the measured features
and often outperform the PMV and adaptive comfort models [31], specifically
when trying to predict the thermal preferences of individuals [19]. Recent
literature employs machine learning models in order to contextualize
environmental data [32, 33, 34, 35] as well as thermoregulation from human
skin through video [36], and physiological data [27, 37, 38]. In addition,
some work has also analyzed the transition time it takes for an occupant to
change its thermal preferences in the same indoor environment [39]. At the
individual level, [19] has shown that data-driven PCM perform better than
conventional models, such as the PMV, on a cohort of 38 occupants. These PCMs
are tailored to an occupant’s specific needs and can be considered as one of
the best approaches to achieving high-performing models for a particular
individual. As for the predicting variable, the 3-point Thermal Preference
scale is a well-established scale used in thermal comfort research as the
label or target variable for data-driven thermal comfort prediction modeling
[19]. Aggregated data-driven models, meaning models that use data from a large
group of people to predict another person’s thermal preference, face the
challenge of multiple occupants having different thermal comfort preference
from each other [40, 41, 12, 42].

####  2.2.2 Cold start prediction

The built environment research is moving towards using a small amount of data
from new occupants in order to successfully predict their thermal comfort
[23]. This scenario is a well-known one when dealing with personalization,
particularly in the field of Recommender Systems and is known as “cold start
prediction”. The cold start personalization problem refers to starting
providing relevant information to the occupant, or a system, as fast and with
as little effort as possible [43]. This is a common practice in smart agents
(e.g., Cortana, Google Assistant, Siri) or services (e.g., Amazon, Netflix)
where occupants can be clustered based on limited knowledge about them [44,
43]. Alternatively, information about the occupants can be obtained by
analyzing historical data and inferring other relevant features [45, 46].
These two techniques can be used together to improve the model prediction
performance.

In the context of health monitoring and well-being label (i.e., mood, health,
stress) prediction, [47] provided a framework to quantify occupant similarity
based on individual behavioral patterns and then cluster them in order to have
groups from which group models can be used for new occupants. [30] used the
Big Five personality trait survey as the one-time general preference survey to
determine groups of occupants in a data-driven manner to then use group-level
information for occupant personalization. Both works rely on physiological,
environmental, and behavioral data paired up with mood, health, and well-being
labels from more than 200 different participants. However, to the best of our
knowledge, this is the first work that explores these techniques in the
context of thermal comfort prediction.

###  2.3 Cohort creation framework

####  2.3.1 Cold start

Personal characteristics captured during the onboarding one-time survey,
include categorical, ordinal, and scalar data that can directly be used to
group occupants. In addition, some standardized surveys allow for their
questions scores to be aggregated, i.e., Highly Sensitive Person Scale (HSPS)
[48] and the Satisfaction With Life Scale (SWLS) [49].

The simple approach to creating cohorts is to use individual one-time survey
responses. Cohorts are created in a straightforward manner by dividing
occupants based on a simple statistic, e.g., median, or creating as many
cohorts as unique response values. Multiple one-time survey responses can also
be combined in a data-driven setup where each survey response is a feature and
a clustering algorithm can automatically group occupants into cohorts based on
the discovered patterns from the questions responses. We determined the
optimal number of cohorts, $k$, by using the clustering metric known as
Silhouette score [50] which ranges from 1 (best score) to -1 (worst score).
The appropriate $k$ is determined by the highest mean value of the Silhouette
score.

Once the cohorts exist, new occupants can be directly assigned to one. If the
cohorts were created in a data-driven manner, the trained clustering algorithm
can then predict the corresponding cohort using the required occupant’s one-
time survey responses. On the other hand, new occupants can be assigned to
cohorts created based on specific values by simply matching its respective
one-time survey response: e.g., if two cohorts are created based on sex, a new
person is either assigned to the Female or Male cohort.

####  2.3.2 Warm start

Responses distribution similarity: Previous work has looked into grouping
occupants solely based on the trend of their thermal comfort responses [22],
for the case of thermal preference, occupants who predominantly have more
prefer cooler responses are grouped together, and so on. Figure 3(b)
illustrates the comparison of two occupants’ thermal preference responses.
Unlike work done in [22], where grouping was done based on the majority
response, the probability distribution of responses is first calculated for
each occupant, on this example, the responses are thermal preference votes
where the values are prefer warmer: -1, no change: 0, prefer cooler: 1. Then,
the occupant’s response distributions are compared using the Jensen-Shannon
(JS) divergence, bounded in $[0,1]$ and symmetric, which calculates the
similarity between two probability distributions. A lower distance value
between occupants means the occupants are more similar.

Cross-model performance: We propose to group people into cohorts based on the
relationship between their responses in the RHRH survey and the different
available environmental and physiological sensor data. [47] suggest that these
relationships can be captured by evaluating the model of one occupant, trained
with its own data, using labeled sensor data from another participant, this is
referred as cross-model performance evaluation. In the context of thermal
comfort, a PCM is a good representation of an occupant’s individual comfort
requirements based on the data of its environment [31]. Hence, using PCMs for
cross-model performance evaluation is an adequate similarity metric. If the
PCM of $occupant_{A}$ has a high performance on data from $occupant_{B}$, it
suggests that both occupants have similarities in how they perceive their
thermal environment. Figure 3(a) shows an example of evaluating the cross-
model performance of two occupants. The data used for the PCM on each occupant
comprise only sensor measurements since any personal characteristic data
collected via a one-time preference survey would have a repeating value for
each data point. For a consistent comparison, each PCM must be trained on the
respective occupant’s data, and any hyper-parameter must be fine-tuned using
the same evaluation metric. Based on the current literature on PCMs where the
target variable comprises 3 ordinal values (i.e., multi-class), one of the
most widely used models is Random Forests [22, 19, 24] and the evaluation
metric is F1-micro score [22, 24] which can be seen as accuracy for the multi-
class classification scenario. F1-micro scores range from 0 to 1, with 1 being
a better prediction accuracy which in turn indicates two occupants are more
similar.

(a) Cold start: Top The occupant is directly assigned to the cohort with the
matching value of the onboarding survey used for cohort creation. Bottom The
clustering algorithm used for cohort creation is used on the participant’s
onboarding survey responses to predict the corresponding cohort.

(b) Warm start: Data points collected from the new participant are evaluated
on each existing Cohort model. The participant is then assigned to the cohort
on which it had the best-averaged performance based on F1-micro score.

Figure 2:  Participant assignment to the existing cohorts depending if the
cohorts were cold start (2(a) or warm start (2(b)). Only 2 cohorts are shown
for illustration purposes.

Finally, both metrics of similarity between occupants, responses distribution
similarity and cross-model performance, can be rearranged into what is called
a similarity or affinity matrix. An affinity matrix is a squared symmetric
matrix where each element to compare, in this case occupants, is both the rows
and columns indices. Each value of the matrix represents the similarity of its
row and column; these values are bounded in $[0,1]$ where $1$ means identical
elements. Affinity matrices have shown to be a useful way of comparing
occupants when the similarity calculations can be transformed into the $[0,1]$
range [47]. Both similarity metrics already match the desired range but the
responses distributions metric have an inverse interpretation where a value of
$0$ means identical occupants. Thus, these values are normalized using the
Radial Basis Function (RBF) kernel (Equation 1) such that now a value of 1
translates to identical occupants.

$\text{RBF
Kernel}\left(Z_{i},c\right)=e^{\frac{-\left(z_{i,j}-c\right)^{2}}{2\mu^{2}}}$
(1)

Where $Z_{i}$ is the similarity metric ( Jensen-Shannon (JS) divergence in
this scenario), $\mu$ is the standard deviation of $Z_{i}$, $z_{i,j}\in Z_{i}$
is the JS divergence between occupants $i$ and $j$, and $c$ is the
normalization center. In order to consider both similarity metrics, both
metrics can be added together with a weight coefficient $\alpha$ and $\beta$
(such that $\alpha+\beta=1$), respectively, to indicate their respective
contribution in the final affinity matrix. At this point, Spectral clustering
[51] is applied to the affinity matrix and, similar to Section 2.3.1, the
adequate number of cohorts, $k$, is determined by the highest mean value of
the Silhouette score. Figure 3 illustrates both similarity metrics
calculation, their rearrangement into an affinity matrix, and cohort
calculation.

(a) Cross-model performance evaluation calculated with F1-micro score, bounded
in $[0,1]$, using each occupants’ PCM with another occupant’ data. The higher
the F1-micro score the more similar the two occupants are.

(b) Responses distribution similarity based on Jensen-Shannon divergence,
bounded in $[0,1]$. After applying an Radial Basis Function (RBF), the higher
the value the more similar the two occupants are.

Figure 3:  Warm start cohort creation approach. 3(a) calculates the cross-
model performance and 3(b) calculates the distance between occupants’
responses distribution. Each approach is weighted before their summation into
an affinity matrix with $\alpha$ and $\beta$ respectively, where
$\alpha+\beta=1$. Spectral clustering is then run on the affinity matrix on a
multiple number of cohorts (e.g., $k=[2,10]$). The best $k$ is the one with
the highest Silhouette Score.

New occupants can be assigned to a cohort only after they complete some RHRH
surveys. However, as depicted in Figure 1(b), collecting the same number of
labeled datapoints, $n$, for each new occupant as it was done with the
existing occupants can be impractical and cost prohibitive. Hence, we propose
to use a “warm start” approach that leverages a much smaller number of data
points $m$ from the new participant, such that $m<<n$. The $m$ datapoints from
the new occupant are used to evaluate the performance of each cohort we
previously created. The performance values are then compared and the occupant
is assigned to the cohort with the highest performance score overall. The
metric used in this last step must be the same as the metric used to determine
the initial cross-model performance evaluation, which for the case of a multi-
class thermal comfort variable is the F1-micro score. Figure 2(b) illustrates
this warm start cohort assignation.

###  2.4 Evaluation

Experiments and modeling parameters are detailed in Table A.1. We used a 0.8
train-test split, participant-wise, on each dataset. Hence, 80% randomly
selected occupants and their data are used to build the cohorts following the
approaches mentioned in the previous section. The remaining 20% of the
occupants are used as test and assigned to a newly created cohort. This entire
process was repeated 100 times for each cohort approach on each dataset used.
Then, once a test occupant is assigned to a cohort, the cohort thermal
preference model is evaluated on the new occupant’s labeled data.

Personal Comfort Models (PCMs) models were computed for cross-model
performance and served as an upper-bound baseline. PCMs are trained with RF as
a the classification model following a 5-fold cross-validation. We also used
the entirety of the available data from the train participants to create an
aggregate model where everyone belongs to one big cohort from which a general-
purpose model can be trained on. This model will serve as a lower bound
baseline. All processes are repeated 100 times in order to minimize biases.

Additionally, the prediction results of PMV at the personal level are
included. The Python package pythermalcomfort [52] is used to compute the PMV
as per the calculations methods in ISO 7730 [53], and the package Scikit-
learn: Machine Learning in Python [54] is used for all the remaining data-
driven calculations. Similar to how it was done in [19], we used the
longitudinal field data (i.e., air temperature, humidity, self-reported
clothing insulation) and the static values (i.e., air velocity=0.1 m/s,
metabolic rate=1.1met) for the PMV calculation unless the values are present
in the dataset. To compare the results on the same 3-point scale, the PMV is
converted into thermal preference classes based on the following assumptions:
$\left|\text{PMV}\right|\leq$ 1.5 is “no change”; PMV $>$ 1.5 is “prefer
cooler”; and PMV $<$ -1.5 is “prefer warmer”. Nevertheless, we acknowledge
that by making these many assumptions and simplifications on the values
themselves, the accuracy of the PMV calculation will be low. We share our code
base for the reproducibility of our analysis on a public GitHub repository:
https://github.com/buds-lab/ccm.

###  2.5 Datasets

In order to assess the proposed framework, two publicly available longitudinal
thermal comfort datasets were used to validate our methodology. One dataset,
named Dorn [55], was collected in Singapore where 20 occupants were asked to
complete approximately 1000 RHRH surveys over a period of 6 months.
Participants were asked to wear a Fitbit smartwatch with the Cozie
[56]111https://cozie.app/ application installed. This application has been
used to collect RHRH surveys [22] and is publicly available for both
Android222https://github.com/cozie-app/cozie and
Apple333https://github.com/cozie-app/cozie-apple platforms. The second dataset
we used was collected from 37 participants over a period of 12 weeks in an
office building located in Redwood City, California, US [57]. Participants
were asked to complete three RHRH online surveys per day while using a
Personal Comfort System (PCS) in the form of an instrumented chair. In this
work we will be referring to the form dataset as the “Dorn” and the latter as
the “SMC” dataset. Both surveys comprised the following overlapping questions:
thermal preferences (using a 3-point scale), clothing, and activity.
Environmental measurements (e.g., dry-bulb air temperature, relative humidity)
were taken using data loggers installed in near proximity (i.e., within a 5-m
radius) from where the participant completed the survey. Additionally, [55]
used Fitbit Versa smartwatches to measure and log physiological data and
iButtons for near-body temperature and humidity. Table 1 shows a brief
overview of both datasets, and Table LABEL:tab:sensors-accuracy shows a
summary of the sensors used and their accuracy. More details regarding the
recruitment criteria and methodology details can be found in the respective
publications.

Dataset |  # Occupants (Sex) |  Age range |  #Responses per participant |  Duration
---|---|---|---|---
Dorn [55] |  20 (10 M, 10 F) |  20 to 55 |  872(min), 1332(max) |  6 months
SMC [57] |  37 (17 M, 20 F) |  25 to 37 |  33(min), 218(max) |  12 weeks
Table 1:  Overview of the two datasets used in this work.

Personal information about the participants (e.g., sex, height, weight) were
obtained asking them to complete an on-boarding survey when joining the
experiment. Additionally, the Dorn dataset included three standardize surveys:
Highly Sensitive Person Scale (HSPS) [48], Satisfaction With Life Scale (SWLS)
[49], and a short version of the Big Five Personality (B5P) test known as Ten-
Item Personality Inventory [29]. These surveys were used to investigate
potential relationships between survey responses from occupants and their
thermal preferences.

###  2.6 Data pre-processing

We time-aligned sensor measurements with the RHRH responses. Thermal
preference response provided by the occupants are used as the ground truth
label for each respective dataset. Feature selection scrutiny was done based
on the latest efforts in data-driven personal thermal comfort prediction [58,
22, 19, 27, 59] and based on what the original dataset papers suggest [57,
55]. A summary of the features used in this paper for model training is shown
in Table 2 for the present study, and Table 2(a) and Table 2(b) for the Dorn
[55] and SMC [57] dataset respectively.

To have comparable results between occupants we decided to use a fixed number
of responses per occupant on each dataset. While this approach does not
address the inherent class balancing issue in thermal preference datasets,
i.e,. having a disproportionate number of data points for each thermal
preference type [60], we decided to prioritize the quantity of available data
points per participants. The Dorn dataset contains a minimum of 872 responses
per participant (Table 1); however, after removing surveys completed outdoors,
in non-transition periods and while participants were exercising, the minimum
number of responses per occupant was 231. [19] found that when the dataset
comes from an environment that all occupants shared during the experiment, the
required number of responses for a stable thermal preference prediction is at
least 60. These results were obtained on the SMC [57] dataset, hence, we opted
to also use the first 60 responses from each occupant as it also covers most
of its participants. This threshold of minimum responses resulted in 35
participants (18 females, 16 males). Figure A.1 in the Appendix shows the
distribution of the filtered thermal preference responses for each participant
on both datasets.

###  2.7 Cohort creation

Following the framework presented in Section 2.3, we created two different
sets of cohorts one for each datasets. Table 3 shows the cold start cohorts
(upper row) and warm start cohorts (lower row) that are constructed based on
the data available in each dataset. As mentioned in Section 2.3.1 the cold
start cohorts are created using only one-time survey responses. In the Dorn
dataset, the onboarding surveys were also used to create other sets of
cohorts. The three surveys (HSPS, SWLS, and B5P) were used together (Surveys
in Table 3) where each survey score is treated as a feature and the number of
cohorts was determined in a data-driven manner via Spectral clustering as
detailed in Section 2.3.1. Moreover, each survey was also used individually.
The HSPS and SWLS surveys, named Sensitive and Life Satisfaction respectively
in Table 3, have an aggregated numerical score which was used to create two
cohorts based on the median value of the occupants’ scores. All participants
were tested but only participants with extreme values on these survey
responses, with a low and high aggregated score, are shown since their results
are higher. Occupants with an aggregated score between the 25th and the 75th
interquartile range were filtered out, which meant only 50% of the total
occupants in the Dorn dataset were used. Since the responses of the B5P cannot
be aggregated into one final score, the number of cohorts based on this survey
was determined in a data-driven manner using each personality score as a
feature. This cohort approach is named Personality in Table 3.

Finally, warm start cohorts are created following the two approaches mentioned
in Section 2.3.2, using both the responses distribution similarity and cross-
model performance combined, and cross-model performance alone (Dist-Cross and
Cross respectively in Table 3). Table A.1 list the different experimental and
modeling parameters. The best performing model, in terms of average expected
F1-micro score, is found using a grid search on the list of hyperparameters.
In the Dorn dataset we determined that the optimal number of cohorts was 2 for
all data-driven approaches. On the other hand, a value of $k$ equal to 2 and 3
was chosen for the Dist-Cross and Cross approaches in the SMC dataset,
respectively. More details on different numbers of cohorts $k$ are shown in
Figure A.2 in the Appendix.

In order to corroborate the usefulness of each cohort approach we also
purposely assigned each occupant to the opposite cluster (if $k=2$ and were
created by cold start approaches) or assigned the occupant to the worst
performing cohort (warm start approaches). This will serve as an ablation
analysis for the cohort approach and, by extension, the data used for its
creation and assignment.

##  3 Results

Feature | Source
---|---
Air temperature | Fixed sensor (indoor)
Relative humidity
Near-body temperature | Wearable sensor
Heart rate
Clothing level | RHRN survey
Sex | One-time survey
Height
Weight
(a) Subset of features chosen for data-driven modeling in the Dorn dataset
based on the original work [55]. Near-body (wrist level) temperature
measurements are found to contain more information than skin temperature [27,
59].

Feature | Source
---|---
Dry-bulb air temperature | Fixed sensor (indoor)
Operative temperature
Relative humidity
Slope in air temperature
Control location | PCS control behavior
Control intensity
Control frequency in the past x (1h, 4h, 1d, 1wk)
Occupancy status
Occupancy frequency in the past x (1h, 4h, 1d, 1wk)
Ratio of control duration over occupancy duration in the past x (x=1 h, 4 h, 1
d, 1wk)
Outdoor air temperature | Outdoor Environment
Sky cover
Weighted mean monthly temperature
Precipitation
Clothing level | RHRN survey
Hour of the day
Day of the week
Sex | One-time survey
Height
Weight
(b) Subset of features recommended for data-driven modeling [19] in the SMC
dataset [57].

Table 2:  Features chosen for data-driven modeling: 2(a) Dorn [55], 2(b) SMC [57] dataset. Features obtained through the one-time survey are dropped for PCMs due to their constant value within each participant.  Type | Cohorts approaches | Data used
---|---|---
Cold start | Sex$\dagger$ | Self-reported value
Surveys | All onboarding surveys (HSPS, SWLS, B5P)
Sensitive |  HSPS scores
Life Satisfaction |  SWLS scores
Personality |  B5P scores
| Agreeableness | Best performing trait from B5P
Warm start |  Responses Distribution and Cross-model performance (Dist-Cross)$\dagger$ |  Thermal preference responses and occupants’ PCM
Cross-model performance (Cross)$\dagger$ | Occupants’ PCM
Table 3:  Cohort approaches chosen. The upper rows are cold start cohorts and
the lower rows are warm start. $\dagger$ were cohorts approaches used on both
datasets.

###  3.1 Overall prediction performance

The performance results, in terms of F1-micro score, of all cohort approaches
from Table 3 in new occupants from their respective datasets are summarized in
Figure 4. Results sets with a tilde (”$\sim$”) as a prefix denote those
approaches where test users were purposely assigned to an incorrect cluster as
detailed in Section 2.7.

Figure 4:  Performance results in F1-micro score of 100 iterations for each
cohort approach on each dataset Dorn and SMC (y-axis) and their baselines
after correct assignment (dark gray) and incorrect assignment (light gray).
Approaches with occupants incorrectly assigned to cohorts have the “tilde”
symbol ($\sim$) before their name. Cold start approaches are highlighted by a
light-blue region and Warm start approaches by a yellow region. The general-
purpose model and PCM median value are highlighted with red dashed lines on
each of their boxplot and filled with red. Additionally, PMV results,
following the calculation in Section 2.4 are shown. Warm start approaches
(Dist-Cross and Cross) have used only one randomly selected data-point. Dorn
dataset: General-purpose boxplot contains 20 values; Sensitive and Life
Satisfaction contain 200 values (2 test occupants at a time $\times$ 100
iterations) and all remaining box plots have 400 values (4 test occupants at a
time $\times$ 100 iterations). SMC dataset: PCM boxplot, contains 35 values
and all remaining boxplots have 700 values (7 test uses at a time $\times$ 100
iterations). The cohort approaches are taken from Table 3.

As mentioned in Section 2.3.2, Warm start cohort approaches like Dist-Cross
and Cross need that new participants complete a few RHRH surveys prior to
assigning them to a given cohort. Different number of labeled data points, or
RHRH, were tested, i.e., 1, 3, 5, and 7, and all of them fall within the
baselines. Figure A.3 shows the results for the Dorn dataset, similar results
were obtained with the SMC dataset. For the purpose of evaluating the minimum
required number of labeled data points, all results in Figure 4 used one
single, randomly selected, data point from each test occupant for assignment.
Compared to using more labeled data points, the performance of using a single
data point still lead to improved results, on average 10% compared to the
general-purpose model.

####  3.1.1 Under performing cohorts

Cohort approaches such as Sex, Surveys, Sensitivity, and Big 5 Personalities
have a median performance below the General-purpose in the Dorn dataset (upper
plot in Figure 4). Although “sex” has been found to have some influence in
thermal preference, dividing occupants based on it does not provide
significant changes, on average, on thermal preference prediction when
compared to a general-purpose model on both datasets. This is supported by the
similar performance of occupants being assigned to their same sex cohort and
opposite sex cohorts (Sex and $\sim$Sex in Figure 4). The lower performance of
two of the Surveys approach components (Sensitivity and Big 5 Personalities,
when used individually, might be the reason for this approach’s lower than
General-purpose performance. We hypothesize the HSPS survey, used for the
Sensitivity was not able to capture enough information due to the geographical
location and participants’ background of the data collection experiment.
Participants from Singapore (Dorn dataset) may have provided a non-accurate
response to this survey since they mostly experience a hot climate, compared
to a full range of cold and hot climates, which could translate into the low
performances of this cohort approach. Each personality trait from the B5P
survey was also tested individually and Agreeableness performed above the
General-purpose value (Figure A.3). This suggests that looking at all
personality traits together (Big 5 Personality) is not as beneficial as
looking into each personality trait individually. Moreover, it is possible
that the small sample size of participants on the Dorn dataset provided little
variability on these survey responses or that the three onboarding surveys
chosen, HSPS, SWLS, and B5P, are simply not relevant for thermal preference.
Overall, the efficacy and usability of these warm start approaches remains an
open question.

####  3.1.2 Above-baseline performing cohorts

The Life Satisfaction is the only survey that by itself has an above General-
purpose performance. This indicates that treating each survey individually
instead of combining them (Surveys) is a better approach for cohort prediction
performance. In fact, only the HSPS and B5P surveys lead to a cohort approach
with favorable performances above the general-purpose models. Since both of
these cohort approaches are related to satisfaction and optimism, we
hypothesize occupants within the resulting cohorts are less prone to
preference changes due to small variations, meaning they have a wider
acceptability range.

On the other hand, cohort approaches Dist-Cross and Cross achieved the highest
median performance on both datasets (top and bottom plots in Figure 4 for the
Dorn and SMC dataset, respectively). While one single labeled data point used
for cohort assignment may be prone to bias, the consistent F1-micro scores
after 100 iterations still positioned both cohort approaches as the top-
performing ones, with a much clearer difference in the Dorn dataset than in
the SMC dataset. Moreover, when each of this cohort approach is compared to
their respective worst assigned cohort, the median performance is reduced by
almost half for Dist-Cross (from 0.61 to 0.32) and Cross (from 0.63 to 0.32)
in the Dorn dataset; and the performance is reduced 4 times for Dist-Cross
(from 0.72 to 0.17) and Cross (from 0.72 to 0.15) in the SMC dataset. The
median performance for Life Satisfaction is also reduced when occupants are
incorrectly assigned (from 0.56 to 0.51). The reduced number of occupants used
for this approach, 10 instead of 20, might be one of the reasons why the
difference in performance is less pronounced when compared to Dist-Cross and
Cross Nevertheless, the Agreeableness cohort approach exhibits different
behavior. Among all 20 occupants in the Dorn dataset, the lowest score
obtained was 4 out of 7 which indicates the occupants rank highly on this
personality trait. As previously mentioned, this personality trait might
influence the acceptable thermal preference range of an occupant and, with
only two cohorts created, both created cohorts could improve an occupant’s
thermal preference prediction. These results suggest that when the cohort
modeling framework is followed correctly, it leads to an overall increase in
median F1-micro performance. This increase is shown to be higher with warms
tart approaches (Dist-Cross and Cross) than with cold start approaches (Life
Satisfaction and Agreeableness).

###  3.2 Occupant-specific improvement

Figure 5 shows a scatter plot of the average percentage changes in occupant-
specific F1-micro score of the above-baseline performing cohort approaches
mentioned in the previous subsection for warm start and cold start types of
cohorts on both datasets.

Figure 5:  Scatter plot of occupant-specific percentage changes in F1-micro
score from the general-purpose performance (General-purpose) to the respective
cohort approach performance. Each dot represents the average value across all
100 iterations for one occupant. A positive value means the occupant benefited
from the cohort approach and a negative one means they are worse-off. For the
cohort approaches in their respective datasets (y-axis) 45%, 60%, 40%, 55%,
35% and 32% of the occupants saw an increase in their thermal preference
prediction performance by relying on their peers’ data, respectively, although
the actual benefit is small.

For each occupant, a positive average percentage change in F1-micro score is
desirable since it indicates an increase in thermal preference prediction
performance when using a cohort model instead of a general-purpose model.
Conversely, a negative value indicates the cohort model fails to provide
useful personalization, and a generic model, which dismisses occupants’
similarity entirely, performs better on average since it was trained with more
data. Overall, in the Dorn dataset around half of the population of occupants
and one-third of the population of occupants in the SMC dataset saw a boost in
prediction accuracy, in terms of F1-micro score, under the cohort modeling
framework in their respective cohort approach. For the remaining occupants,
there is little change in performance. In Figure 4 the cohort Life
Satisfaction scored marginally better than the General-purpose (0.55 to 0.52)
and the Agreeableness approached score a slightly higher median performance
(0.58). This is highlighted in Figure 5 Dorn:Life Satisfaction where 40% of
occupants saw a performance boost of less than 1%, whereas in
Dorn:Agreeableness 55% of occupants saw a performance boost of around 2%.

Dist-Cross and Cross approaches show a performance boost of up to 36% and 46%,
and an average of 5% and 8%, on the Dorn and SMC dataset respectively.
Overall, the number of occupants that benefited from these approaches are more
than half (45% and 60%) and one-third (35% and 32%), respectively on each
dataset. The number of occupants that are better-off from using Dist-Cross or
Cross are different on each dataset. The Dist-Cross approach benefits more
occupants than the Cross approach on the SMC dataset, where the data was
collected in an office building across many multiple shared spaces on two
different floors. Under these circumstances, personal thermal preferences
captured by the feedback distribution (the Dist component of Dist-Cross)
contribute to more occupants being better-off by Dist-Cross compared to not
using this information (Cross approach). On the other hand, in the Dorn
dataset all occupants provided their thermal preference response in their own
work environments with no guarantees of similar conditions and exposures
amongst all of the spaces. Occupants’ feedback distribution (the Dist
component of Dist-Cross) was not able to capture enough preference
characteristics that could be quantified and compared among the occupants,
resulting in more occupants being better-off with the Cross approach alone.
Nevertheless, it is also important to highlight that some occupants are
actually worse-off regardless of the cohort approach used, and their average
percentage performance is reduced. The entire distribution of percentage
changes across all 100 iterations for these approaches and for each occupant
can be found in Figure A.4 and A.5 for the Dorn and SMC dataset respectively.

Finally, metadata about the participants who are better-off and worse-off is
summarized in Table A.2. Overall, there are no major differences between the
sex and mean and standard deviation of height and weight among the occupants
in both groups, in most cohort approaches. The cohort approach of Life
Satisfaction on the Dorn dataset and Cross on the SMC dataset show a slight
majority of Female and Male better-off occupants, respectively. Nevertheless,
due to the small sample size of occupants on both datasets it is not possible
to generalize these findings.

##  4 Discussion

We showed that CCM can improve the thermal preference prediction of a new
occupant by using other occupants’ data who are grouped based on preference
similarity. We determined that: i) Warm start cohort approaches outperform
cold start approaches at the cost of requiring labeled data points from each
new occupant; ii) while around half and one-third of occupants, in each Dorn
and SMC dataset respectively, benefited from being assigned to a cohort via
warm start and using its cohort model, the remaining occupants saw a very
little variation in their performance for both being better-off or worse-off;
and iii) the applicability of cohorts is dependent on the availability of data
from the occupants as well as the number of occupants willing to provide it,
as we found ourselves able to investigate more cohort approaches on the
dataset with more sensed modalities and more one-time onboarding surveys, like
in the Dorn dataset.

###  4.1 Warm vs cold start

Warm start cohorts are bottom-up approaches that focus on the granular data
with a direct connection with thermal preference, i.e., relying on feedback
distribution and cross-model performance, whereas cold start cohorts are a
top-bottom approach that relies on the set of questions used to showcase the
separability of personal thermal preferences. It can be argued that the latter
approaches were expected to underperform since by their definition,
information regarding the actual thermal preference of occupants, e.g., the
labeled data points, was not used to create the cohorts. Nevertheless, recent
literature in personalized healthcare monitoring [47, 30] highlights the
usability of these sources of information as primary ways to leverage other
occupants’ data. We found that the SWLS surveys, or Life Satisfaction, and the
personality trait of Agreeableness from the B5P survey lead to cohorts where
thermal preferences are shared, leading to slightly improved thermal
preference prediction when compared to general-purpose models. One advantage
of using this information is that allows a cold start from a new occupant.

However, the notion of “no historical data” is not a harsh threshold since
depending on the amount of data available, some work still considers a cold
start when very little historical data is used [46], i.e., less than 10 data
points [47]. While work done in [23] explores a Bayesian approach to cluster
occupants based on historical measured data and then uses the cluster’s
thermal comfort models with as little as 8 data points which also falls under
the cold start definition used in [47], to the best of our knowledge our
proposed framework is the first to not only look at measured data streams to
find occupant similarity but includes the aforementioned mechanisms from other
fields. Our framework allows for a full cold start prediction and a warm start
prediction with as little as one single historical data point. Both types of
cohort approaches have shown that while only some occupants benefited from
this framework, the majority experience very little variability in
performance, either better-off or worse-off. Nevertheless, this also shows
that our framework allows for prediction performance just as good as the
general-purpose models but with at least half less training data, for the case
of only two cohorts.

###  4.2 From “how much can you benefit” to “who can benefit”

The warm start cohorts at the occupant level produced an overall average
increase of up to 8% (Dorn:Dist-Cross) and 5% (SMC:Dist-Cross) on the Dorn and
SMC dataset respectively among the occupants who were indeed better-off. These
numbers changed to 4% (Dorn:Dist-Cross) and 2% (SMC:Dist-Cross) when all
participants are considered in the calculation. However, on both datasets
there are occupants who could significantly take advantage of these approaches
with performance increases as high as 45%, but also occupants who saw a
decrease in their prediction performance. While this scenario is not uncommon
when dealing with group-level occupant personalization [30], cold start
approaches like our framework offers the possibility to identify these better-
off and worse-off occupants even before the occupant starts occupying the
building.

From a facility manager’s perspective, being able to identify if an occupant
will be worse-off, or better-off, on a specific thermal zone with another
group of occupants can help prioritize certain occupants, avoid complaints,
and quantify the need for PCS. The trend of hot-desking is not well received
among occupants [61] and can show a negative effect on work engagement, job
satisfaction, and fatigue [62]. Thus, a more suitable approach would require
engaging with specific occupants and adapt their needs. On the other hand,
although the results here are limited to two datasets, it is possible that
some occupants are inherently more difficult to predict than others, and
pinpointing back to these occupants alone is equally important. Be that as it
may, cold start approaches that rely on one-time surveys, such as cold start
cohort approaches, raise data privacy concerns. Facility managers and building
practitioners must take the necessary precautions when dealing with such data
from its occupants.

###  4.3 Limitations and future work

The evaluation of our proposed framework based on one-time onboarding surveys
and sensor measurements as features has some limitations. First, the amount of
occupants on each dataset is 10 times smaller than those used in related work
in the healthcare domain [30, 47] and in a related thermal comfort cluster
prediction [23]. Additionally, one of the two datasets used (Dorn dataset)
lacks seasonal variability because it was collected in Singapore (tropical
climate), whereas the other dataset (SMC) which included both seasons and a
more diverse group of occupants, lacks the one-time surveys required by the
framework. While multiple datasets can be used together as one much bigger
dataset, like the ASHRAE Thermal Comfort Database II [17], the methodology and
available measurements should overlap, which is not the case for the recent
field data collection experiments.

Second, the models and features used were not chosen after an exhaustive
feature and model selection pipeline. One could argue that features extracted
from the physiological time series measurements from each occupant, matched
with their thermal preference responses, could contain useful information
which could in turn be used for comparison across occupants and subsequent
cohort creation. While extracting those temporal attributes is out of the
scope of the present work, the modularity of the framework and its data-
agnostic proposition makes the inclusion of these newly learned or discovered
features, or any other new modalities, plausible.

Third, in our evaluations, we considered that once an occupant is assigned to
a cohort, it remains its membership for the duration of the evaluation
experiment. It is likely that influenced by external factors, an occupant
might be better-off in terms of thermal preference prediction if it is
assigned to a different cohort in certain settings. Future work can build on
this cohort framework and insert the respective adaptive mechanisms that allow
occupants to change cohorts as time goes by. Particularly, when coupled with
the energy repercussions of using cohort models to predict thermal preference,
an entire feedback loop from the occupant to the system could enable this
online incremental learning.

Aware of the context-dependent nature of our framework and its components, we
do not claim generalizable results. Ideally, a dataset with a large enough
group of people where all occupants undertake the same one-time onboarding set
surveys, with overlapping sensor measurements, could provide more
generalizable insights. Also, if multiple datasets from different contexts are
used, features that represent the location, climate, and building type could
be considered as part of the modeling portion of the framework. However, the
presented framework is modular enough to easily adapt to new sets of features
either as part of the cohort models themselves or as part of how the cohorts
are created. We plan to investigate the results on more diverse datasets with
occupants from different backgrounds and populations.

##  5 Conclusion

Cohort Comfort Models (CCMs) are a new approach to thermal comfort modeling
that builds on Personal Comfort Models (PCMs). Cohort models leverage the
previously collected data from other occupants to predict an occupant’s
thermal preference based on similarities with other occupants. The advantage
of this method in two different datasets shows that the proposed framework for
cohort creation and occupant assignment lead to half and one-third of the
occupants in each dataset experiencing an average increase of 5% and 8%,
respectively, in their thermal comfort prediction performance with as little
as a single labeled data point required from the new occupant when compared to
general-purpose data-driven models.

Unlike related literature focused on occupant segmentation for better thermal
preference prediction, our proposed framework offers the ability to identify
the occupants who will be better-off from said cohort approach, and those who
might not, based on the occupant’s background information or, at most, a
single labeled data point from the occupant at the building premises. We
provided the framework in a data and site-agnostic manner and described its
different implementations depending on what information is available with the
scalable potential to various data streams. Cohort comfort models can benefit
the building industry by improving the level of thermal comfort among
occupants without the need to rely on individual customization which can be
unfeasible and too expensive. Further global data collection experiments with
multiple occupants are encouraged to investigate the generalization of the
potential different cohorts and the potential discovery of thermal preference
signatures shared across different occupant populations. Additionally, we
encourage more research on control strategies that take advantage of the
cohort-customization instead of individual-level catering.

## CRediT author statement

Matias Quintana: Conceptualization, Methodology, Software, Validation, Formal
analysis, Investigation, Visualization, Writing - Original Draft. Stefano
Schiavon: Conceptualization, Visualization, Supervision, Writing - Reviewing
and Editing, Funding acquisition. Federico Tartarini: Resources, Data
Curation, Writing - Reviewing and Editing. Joyce Kim: Resources, Data
Curation, Writing - Reviewing and Editing. Clayton Miller: Conceptualization,
Resources, Visualization, Supervision, Project administration, Writing -
Reviewing and Editing, Funding acquisition.

## Acknowledgements

This research was funded by the Republic of Singapore’s National Research
Foundation through a grant to the Berkeley Education Alliance for Research in
Singapore (BEARS) for the Singapore-Berkeley Building Efficiency and
Sustainability in the Tropics (SinBerBEST2) Program and a Singapore Ministry
of Education (MOE) Tier 1 Grant (Ecological Momentary Assessment (EMA) for
Built Environment Research). BEARS has been established by the University of
California, Berkeley as a center for intellectual excellence in research and
education in Singapore. The authors would like to acknowledge the team behind
the data collection and processing, including Mario Frei, Yi Ting Teo, and Yun
Xuan Chua.

## Appendix A

Parameter | Description or value
---|---
Train-test ratio | 0.8, participant-wise
Model | Random Forest
Model Hyperparameters |  Number of trees: 100, 300, 500 Split criterion: Gini index Tree depth: 1, $\dots$, 10 Min. smaples for split: 2, 3, 4 Min. samples on an edge : 1, 2, 3
Model training |  Hyperparameter grid search 5-fold Cross-Validation
Model metric | F1-micro score
Clustering |  Number of cohorts tested: $k\in[2,10]$ Metric: Silhouette score
Iterations | Modeling pipeline is repeated 100 times
Table A.1: Experiments and modeling parameters

(a) Dorn [55] participants and their first 231 responses per participants.

(b) SMC [57] participants and their first 60 responses per participants

Figure A.1:  Thermal preference response distribution for all participants in
both datasets. 1(a) shows the Dorn [55] dataset and 1(b) shows the SMC [57]
dataset.

(a) Average Silhouette scores for cold start (Surveys and B5P) and warm start
(Dist-Cross and Cross) approaches on the Dorn dataset.

(b) Average Silhouette scores for warm start approaches on the SMC dataset.

Figure A.2:  Number of cohorts determined based on the average Silhouette scores at different number of cohorts $k$ ($k\in[2,10]$) on both datasets. The higher the Silhouette score the better. 2(a) and 2(b) showcase the values for the Dorn and SMC datasets respectively.  Figure A.3:  Performance results in F1-micro score of 100 iterations for each cohort approach on the Dorn dataset (y-axis). Cold start approaches are highlighted by a light-blue region and Warm start approaches by a yellow region. These results are complementary to Figure 4. Each personality trait from the B5P (Extraversion, Agreeableness, Conscientiousness, Emotional stability, Openness to experiences) are considered individually as cold start approaches. Warm start approaches, Dist-Cross and Cross, have the number of labeled data points used for assignation; i.e., 1, 3, 5, and 7; as suffixes.  Figure A.4:  Performance percentage change in F1-micro score of 100 iterations for each occupant in each cohort approach (y-axis) for the Dorn dataset. The percentage change is calculated based on the respective cohort approach performance and the General-purpose performance. The average value of each occupant is reported in Figure 5.  Figure A.5:  Performance percentage change in F1-micro score of 100 iterations for each occupant in each cohort approach (y-axis) for the SMC dataset. The percentage change is calculated based on the respective cohort approach performance and the General-purpose performance. The average value of each occupant is reported in Figure 5.  Cohort Approach | Better/Worse | Sex | Height (m) | Weight (kgs)
---|---|---|---|---
Dorn:Dist-Cross | Better-off | 5M, 4F | 168$\pm$4.71 | 64.3$\pm$12.04
Worse-off | 5M, 6F | 167$\pm$10.63 | 64.55$\pm$14
Dorn:Cross | Better-off | 6M, 6F | 169.08$\pm$8.95 | 64.08$\pm$12.99
Worse-off | 4M, 4F | 166$\pm$7.38 | 65$\pm$13.38
Dorn:Life Satisfaction | Better-off | 1M, 3F | 165.25$\pm$8.95 | 55.25$\pm$5.76
Worse-off | 3M, 3F | 170$\pm$5.86 | 70.17$\pm$8.53
Dorn:Agreeableness | Better-off | 7M, 4F | 167.18$\pm$8.70 | 65.82$\pm$14.33
Worse-off | 3M, 6F | 168.67$\pm$8.16 | 62.78$\pm$11.34
SMC:Dist-Cross | Better-off | 8M, 4F | 169.58$\pm$11.50 | 74.92$\pm$15.77
Worse-off | 8M, 14F | 166$\pm$8.85 | 73.59$\pm$17.02
SMC:Cross | Better-off | 9M, 2F | 174$\pm$7.36 | 78.36$\pm$18.86
Worse-off | 7M, 16F | 164.04$\pm$9.50 | 72$\pm$14.98
Table A.2: Breakdown of occupants’ metadata based on the cohort approaches
they are better or worse-off. Height and Weight columns display the mean $\pm$
standard deviation

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  *[CCMs]: Cohort Comfort Model
  *[CCM]: Cohort Comfort Model
  *[PCMs]: Personal Comfort Model
  *[PMV]: Predicted Mean Vote
  *[PCM]: Personal Comfort Model
  *[RHRH]: Right-Here-Right-Now
  *[HSPS]: Highly Sensitive Person Scale
  *[SWLS]: Satisfaction With Life Scale
  *[JS]: Jensen-Shannon
  *[RBF]: Radial Basis Function
  *[RF]: Random Forest
  *[PCS]: Personal Comfort System
  *[B5P]: Big Five Personality