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Results in Engineering 22 (2024) 102148
Available online 18 April 2024
2590-1230/© 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-
nc-nd/4.0/).
Machine learning-based predictive model for thermal comfort and energy
optimization in smart buildings
Youssef Boutahri
*
, Amine Tilioua
**
Research Team in Thermal and Applied Thermodynamics (2.T.A.), Mechanics, Energy Efciency and Renewable Energies Laboratory (L.M.3.E.R.). Department of
Engineering Sciences, Faculty of Sciences and Techniques Errachidia, Moulay Ismaïl University of Mekn` es, B.P. 509, Boutalamine, Errachidia, Morocco
ARTICLE INFO
Keywords:
Thermal comfort
Energy efciency
HVAC systems
Machine learning
Model predictive control
Smart building
ABSTRACT
In the current context of energy transition and increasing climate change, optimizing building performance has
become a critical objective. Efcient energy use and occupant comfort are paramount considerations in building
design and operation. To address these challenges, this study introduces a predictive model leveraging Machine
Learning (ML) algorithms. The model aims to predict thermal comfort levels and optimize energy consumption in
Heating, Ventilation, and Air Conditioning (HVAC) systems. Four distinct ML algorithms Support Vector Machine
(SVM), Articial Neural Network (ANN), Random Forest (RF), and EXtreme Gradient Boosting (XGBOOST) are
employed for this purpose. Data for the model is collected using a network of Raspberry Pi boards equipped with
multiple sensors. Performance evaluation of the ML algorithms is conducted using statistical error metrics,
including, Root Mean Square Error (RMSE), Mean Square Error (MSE), Mean Absolute Error (MAE), and coef-
cient of determination (R2). Results reveal that the RF and XGBOOST algorithms exhibit superior performance,
achieving accuracies of 96.7 % and 9.64 % respectively. In contrast, the SVM algorithm demonstrates inferior
performance with a R2 of 81.1 %. These ndings underscore the predictive capability of the RF and XGBOOST
model in forecasting Predicted Mean Vote (PMV) values. The proposed model holds promise for enhancing
occupant thermal comfort in buildings while simultaneously optimizing energy consumption in HVAC systems.
Further research could explore the practical applications of these ndings in building design and operation.
1. Introduction
Lately, many countries are responding to the Sustainable Develop-
ment Goals (SDGs) set by the United Nations in 2015. The 7th goal SDG-
7 underscores the imperative of transitioning towards clean and
renewable energy, SDG-12 advocates for sustainable consumption and
production of natural resources, including electricity, and SDG-13 urges
action on greenhouse gas emissions [1]. Therefore, its crucial to consider
energy production and consumption of every city. The energy con-
sumption of buildings is responsible for a signicant amount of energy
used in cities [2]. Notably, industry building, whose consumption
comprises about 40 % of the world’s primary energy consumption [3].
Among various components within buildings, HVAC systems dominate,
representing over half of the used energy [4]. It is crucial to recognize
that HVAC systems, including associated activities, contribute signi-
cantly to overall building energy consumption. Consequently, reducing
energy demand from HVAC systems becomes paramount to promote
energy conservation and mitigate greenhouse gas emissions. Ventilation
systems play a crucial role in this regard, with commercial and industrial
buildings losing substantial amounts of heat through ventilation mech-
anisms [5]. To address these challenges, focusing on optimizing and
improving HVAC systems is crucial. The reduction of energy consump-
tion associated with HVAC systems can be done according to energy
efciency measures techniques and by taking account of technological
advancements. This not only preserves energy resources but also con-
tributes to the collective effort of minimizing greenhouse gas emissions
and addressing climate change concerns. Therefore, the development of
innovative solutions for ventilation and HVAC systems becomes an
essential step towards achieving sustainable and environmentally
friendly building practices. The quality of the indoor environment is
critical for the health, well-being, and productivity of building occu-
pants. Key factors inuencing indoor environmental quality (IEQ)
include visual comfort, thermal comfort, acoustics, and indoor air
quality (IAQ) [6]. Among these factors, thermal comfort has been
* Corresponding author.
** Corresponding author.
E-mail addresses: [email protected] (Y. Boutahri), [email protected] (A. Tilioua).
Contents lists available at ScienceDirect
Results in Engineering
journal homepage: www.sciencedirect.com/journal/results-in-engineering
https://doi.org/10.1016/j.rineng.2024.102148
Received 8 January 2024; Received in revised form 19 March 2024; Accepted 16 April 2024

Results in Engineering 22 (2024) 102148
2
identied as particularly important, as it directly affects occupant pro-
ductivity [7–9]. Research indicates that increased interior temperatures
can lead to reduced mental performance due to under-arousal and
decreased physical activity [10]. To regulate indoor thermal conditions
effectively and potentially improve occupants’ intellectual abilities and
productivity, monitoring occupant thermal comfort becomes crucial.
Comfort-driven and energy-aware HVAC systems have been found to be
efcient in achieving this goal, resulting in energy savings ranging from
4 % to 32 % [11]. However, the actual energy savings achieved depend
on factors such as building size, construction materials, building type,
and climate conditions [12,13]. By giving priority to the thermal com-
fort of occupants and implementing energy-efcient HVAC systems, it is
possible to improve the indoor environment quality and to create a
healthy, comfortable and productive environment for building occu-
pants. The primary objective of HVAC systems is to create a thermally
pleasant indoor atmosphere. Current thermal comfort models, such as
the ASHRAE 55 standard [14], aim to achieve this by considering factors
like temperature, relative humidity, air velocity, and human variables
based on the PMV.
In recent years, the concept of human-centered operation of HVAC
systems has gained signicant interest [15]. This approach has the po-
tential to enhance energy efciency by prioritizing thermal comfort for
occupants and reducing energy consumption. Real-time evaluation of
thermal comfort is of utmost importance in enhancing indoor environ-
ments for building occupants. Considering that individuals spend
approximately 90 % of their time indoors, with nearly 70 % of that time
in residential settings [16], it is crucial to leverage advancements in
Model Predictive Control (MPC) [17] and ML [18] to optimize building
operations and create sustainable and comfortable indoor environments.
ML, also known as predictive analysis or statistical learning, combines
articial intelligence, statistics, and computer science [19]. It has
become a fundamental tool in various domains, ranging from autono-
mous driving to medical diagnosis. In the eld of building management,
ML techniques have gained attention for modeling, fault detection, en-
ergy consumption, thermal comfort, and more [20]. These techniques
have been used to forecast indoor climates, occupant behaviors, energy
consumption, and weather conditions in buildings [21–25]. Addition-
ally, ML algorithms have been employed to predict thermal comfort,
both for individuals and groups of occupants, offering potential solu-
tions for managing building energy consumption effectively. While ML
shows promise in predicting thermal comfort and optimizing energy
consumption in buildings, further research is needed before widespread
application can be realized. In conclusion, the utilization of ML tech-
niques holds great potential for predicting thermal comfort and opti-
mizing energy consumption in buildings. The importance of giving
priority to the well-being of occupants and the contribution of techno-
logical advancements allow to create more sustainable and comfortable
indoor environments. In this study, an intelligent ML prediction model
was proposed to optimize thermal comfort control in buildings. The
approach involves data acquisition from on-site measurements and
resident questionnaires conducted in ofce buildings. The principal
contributions of this study are as follows:
Data pre-processing: Data pre-processing is essential to improve the
quality and reliability of the data. In this approach, the Pearson corre-
lation method was used to extract the parameters that exhibit the
highest correlation with PMV. This renement renders them more
conducive to streamlined model training and accurate predictive results.
Development of an intelligent ensemble ML model: ML algorithms
were used, in particular SVR, RF, XGBOOST and ANN, to predict the
thermal comfort indicator known as PMV.
Evaluation of predictions: The performance of this model is evalu-
ated based on its ability to predict the thermal comfort of occupants
using the PMV indicator. This evaluation provides an overview of the
model’s effectiveness in accurately estimating the level of thermal
comfort experienced by building occupants.
The present study aims to optimize thermal comfort control in
buildings, via an intelligent ML model developed using a comparison of
various methodologies and evaluating the predictions based on the PMV
indicator. The results of this research may have practical implications
for intelligent MPC design, thermal comfort control system design
methodology and building energy efciency. The remainder of this
paper is presented as follows: In Section 2, related work is addressed.
Section 3 describes the design methodology of the thermal comfort
control system. The materials and methods are covered in Section 4,
while Section 5 presents the results and related discussions. Section 6
provides the conclusion of the paper.
2. Related works
The prediction outcomes of ML algorithms have previously been
applied by numerous researchers towards the optimization of the inte-
rior thermal environment [26,27]. Some of them have optimized indoor
conditions and energy efciency in buildings utilizing ML techniques to
MPC. Lui et al. [28] employed ANN and GBDT algorithms to develop a
model for forecasting the behavior of occupants in the use of HVAC
systems. Yang et al. [29] employed a Recurrent Neural Network (RNN)
with a nonlinear autoregressive network topology with exogenous in-
puts to construct an MPC to operate air-condition mechanical ventila-
tion (ACMV) systems. Deng et al. [30] Built and validated a
Reinforcement Learning (RL) based occupant behavior model for ther-
mostat and garment level setting in a single building, then transfer the
model to other buildings with different HVAC management systems.
Zhang et al. [31] employed two basic interpretable ML techniques,
linear regression (LR) and decision tree (DT), to create the surrogates for
thermal comfort models. Zhang et al. [32] conducted data analysis
using building data provided by the IoT to develop a precise thermal
comfort model for smart building control. To explore the connection
between controlled building activities and thermal comfort, they
employed a Deep Neural Network (DNN) for simulation. The study also
investigated the impact of different DNN topologies on the results. Gan
et al. [33] introduced a computerized framework that combines build-
ing information modeling (BIM) and data-driven ML models to assess the
optimal thermal comfort of indoor spaces, considering the inuence of
natural ventilation. Gao et al. [34] presented Deep Comfort, a frame-
work for thermal comfort control in buildings using deep reinforcement
learning. They approached the problem by considering thermal comfort
as a cost reduction challenge, taking into account both HVAC energy
consumption and occupant satisfaction. To estimate occupants’ thermal
comfort, they utilized a deep forward neural network algorithm with
Bayesian regularization. Additionally, they proposed the application of a
deep deterministic policy gradient (DDPG) approach to learn the policy
for controlling thermal comfort. H. Yan et al. [35] provided an inte-
grated system to anticipate and optimize building performance in
Singapore for two types of residential structures. Their results demon-
strate that the XGBOOST algorithm works best in terms of transfer
learning, with R2 =0.95 for point block buildings. K. Huang et al. [36]
created a ML prediction model for the thermal comfort of passengers
within subway compartments. The algorithms employed in this paper
are LR, RF, SVM, and DT. Their results demonstrate that RF is the best
performing algorithm, with an R2 of 0.6607 for predicting the TSV value
of passengers inside the subway.
3. Methodology
3.1. Thermal comfort
Thermal comfort is of paramount importance in the building design
process due to the considerable amount of time individuals spend in-
doors. The American Society of Heating, Refrigerating, and Air-
Conditioning (ASHRAE) denes thermal comfort as the cognitive
expression of satisfaction within one’s immediate thermal surroundings
[14]. However, quantifying and analyzing thermal comfort poses
Y. Boutahri and A. Tilioua

Results in Engineering 22 (2024) 102148
3
challenges as it is subjective and varies among individuals. Various
factors, such as the building’s physical structures, indoor and outdoor
thermal conditions (e.g., weather), and heat sources (e.g., bulbs, com-
puters), collectively inuence the thermal environment within a build-
ing [34]. The HVAC system plays a crucial role in adjusting air
temperature and humidity set-points to regulate the building’s thermal
conditions, thereby affecting energy consumption. The PMV index,
calculated using Eq. (1), serves to estimate the thermal comfort level of
a group of individuals based on a seven-point thermal sensation scale as
shown in Table 1. Occupants of the building act as subjective evaluators
of thermal comfort within the indoor environment. To establish
acceptable thermal comfort zones for a large population, numerous re-
searchers have investigated factors inuencing thermal comfort [37,38].
The PMV model, developed by Fanger in the 1970s, is widely
acknowledged as a thermal comfort model [39]. This model in-
corporates six parameters, relative humidity, indoor air temperature,
clothing, mean radiant temperature, air velocity, and occupant meta-
bolic rate to assess thermal comfort. Fig. 1 provides an overview of the
personal and environmental variables impacting thermal comfort (see
Table 2).
The PMV index plays a critical role in maintaining comfortable and
energy-efcient indoor environments, which are crucial for the well-
being, comfort, and productivity of occupants. The PMV index quan-
ties the thermal comfort experienced by individuals based on various
environmental factors such as temperature, humidity, air velocity, and
clothing insulation. It can be calculated using the following equations
[39,40]:
PMV =[0.303.exp(− 0.036.M)+0.028].L(1)
where
L= (M−W) − 3.05.10−3.
[5733 −6.99.(M−W) − Pa] − 0.42.[(M−W) − 58.15]
.M.(5867 −Pa) − 0.0014.M(34 −ta)−
3.96.10−8.fcd .(tcl +273)4− (tr+273)4−fcl.hc.(tcl −ta)
(2)
tcl =35.7−0.028 .(M−W)
−Icl.3.96⋅10−8⋅fcl ⋅(tcl +273)4− (tr+273)4
−Icl⋅fcl ⋅hc⋅(tcl −ta)
(3)
hc=2.38.|tcl −ta|0.25 ,if 2.38.|tcl −ta|0.25 >12.1
var
√
12.1 var
√,if 2.38.|tcl −ta|0.25 ≤12.1
var
√(4)
fcl =1+1.29.Icl,if Icl ≤0.078
1.05 +0.645.Icl if Icl >0.078 (5)
Pa=hr.6.1094.exp (17.625.ta)
(ta+243.04)(6)
Where:
PMV: Predicted mean vote index value;
M: metabolic rate (W/m
2
);
L: Thermal load of human body (W/m
2
);
W: Rate of mechanical work (W/m
2
) which is 0 in most activities;
Pa: Partial water vapor pressure (Pa);
ta: Indoor air temperature (◦C);
tcl: Surface temperature of clothing (◦C);
tr: Mean radiant temperature (◦C);
fcl: Clothing surface area factor;
Icl: Thermal resistance of clothing (m
2
◦C/W);
hc: Convective heat transfer coefcient (W/m
2
◦C);
var: Air velocity (m/s);
hr: Relative humidity (%).
3.2. Indoor air quality
The term IAQ describes how many pollutants, how humid the air is,
and how hot or cold it is inside. Some of these pollutants, such as carbon
dioxide (CO
2
), smells, and dust or smoke particles, are noteworthy.
Despite the fact that CO
2
is not the sole pollutant in the air, its average
concentration in parts-per-million (ppm) is frequently used to gauge IAQ
in buildings [41]. Because of this, a good ventilation system can main-
tain a satisfactory IAQ, which not only satises health requirements but
also provides a better setting to ensure a cozy and odor-free atmosphere.
Regarding CO
2
levels and their limitations, there are several interna-
tional legislations.
3.3. Thermal comfort management system
Designing a thermal comfort control system presents two main
challenges. The rst challenge concerns the distinct nature of thermal
comfort experienced by occupants. The second challenge involves
managing the energy costs associated with operating the HVAC system.
The rst measure is the energy cost attributed to the operation of the
HVAC system, encompassing heating, ventilation, and air conditioning.
The second measure focuses on evaluating the thermal comfort levels
experienced by occupants. The overarching goal of this control system is
twofold: to enhance occupants’ thermal comfort and concurrently
minimize energy expenses. Consequently, the main objective of such a
model is to develop an articial intelligence-based system aimed at
achieving a harmonious compromise between achieving the best
possible thermal comfort for occupants and guaranteeing energy ef-
ciency. Considering both thermal comfort and energy costs is crucial in
the design and implementation process. Fig. 2 illustrates the essential
steps involved in designing a thermal comfort control model.
This paper proposes using ML algorithms to forecast the PMV index.
This prediction is subsequently integrated into an advanced intelligent
control system to regulate thermal comfort within buildings while
minimizing energy consumption of HVAC systems. To achieve this, a
sensor network is deployed to collect fundamental data on the indoor
environment. These data undergo preprocessing to determine their
correlation with thermal comfort parameters, facilitated by the use of a
correlation matrix.
The ML algorithms used, including RF, SVR, ANN, and XGBOST, are
then applied to develop an environmental prediction model for intelli-
gently forecasting indoor environmental conditions. This model is sub-
sequently used to formulate effective control strategies for HVAC
systems, aiming to optimize their operation while maintaining occu-
pants’ thermal comfort.
The model that achieves the highest R2 score, indicating its superior
ability to explain the variance in the data, and simultaneously exhibits
the lowest RMSE value, denoting its minimal prediction error, is care-
fully selected as the pivotal component of the intelligent control system.
This meticulous selection process ensures that the chosen model not only
accurately captures the nuances of the indoor environmental conditions
but also reliably predicts thermal comfort levels, thereby optimizing the
system’s performance in real-world applications. Once integrated into
the control system, this model adjusts setpoints in real-time to ensure
optimal thermal comfort. Furthermore, the system undergoes contin-
uous renement using daily operational data from HVAC systems,
allowing for ongoing optimization of performance and precision. The
detailed architecture of the model is illustrated in Fig. 3 and provides a
comprehensive description of the proposed model, thus allowing for a
thorough understanding of its operation and integration into the overall
Table 1
Seven-level thermal sensation scale (Reference: Adapted from ISO 7730–2005).
Sensation Cold Cool Slightly
cool
Neutral Slightly
warm
Warm Hot
Vote ¡3 ¡2 ¡1 0 1 2 3
Y. Boutahri and A. Tilioua

Results in Engineering 22 (2024) 102148
4
control system.
4. Materials and methods
4.1. Building selection
The buildings selected for this study are ve Leadership in Energy
and Environmental Design (LEED) buildings and eight Conventional
buildings ofce (CBs), giving a total of 13 buildings [42]. Fig. 4 shows an
example of the buildings selected for the study. All the buildings are
situated in Amman, a city in north-central Jordan, and experience
identical weather patterns characterized by a lengthy, scorching sum-
mer and a brief, chilly winter. During the winter season, the relative
humidity reaches approximately 70 %, while in the summer months, it
ranges between 30 % and 40 %.
4.2. Data collected
The dataset employed in this study was gathered by R. Elnaklah
[42] at the University of Bath in Amman, Jordan, spanning the period
from summer 2017 to winter 2019. It comprises a comprehensive
collection of data encompassing various aspects related to thermal
comfort and indoor air quality. The dataset consists of the following
components:
Objective Data: The dataset includes objective measurements of four
key thermal condition indicators, namely relative humidity, air velocity,
air temperature, and mean radiant temperature. Additionally, it in-
corporates data on indoor air quality, specically carbon dioxide con-
centration levels. These objective indicators offer quantitative insights
into the environmental conditions within the buildings.
Subjective Data: In addition to objective measurements, the dataset
incorporates subjective data obtained through post-occupancy
Table 2
PCC values for parameters highly correlated with PMV.
FEATURE PMV PPD TR TA MET AV RH CLO
PEARSON COEFFICIENT 1 0.823 0.653 0.620 0.523 −0.319 0.303 0.220
Fig. 1. Environmental and personal factors that inuence thermal comfort.
Fig. 2. Development and implementation of thermal comfort model.
Y. Boutahri and A. Tilioua

Results in Engineering 22 (2024) 102148
5
evaluation (POE) surveys. These surveys capture occupants’ perceptions
and evaluations of their thermal comfort experiences. Furthermore,
absenteeism/presenteeism data, which records instances of absence or
presence related to thermal comfort conditions, are included. The sub-
jective data provides valuable insights into occupants’ subjective expe-
riences and perceptions of thermal comfort.
Thermal Comfort Indicators: The dataset encompasses various ther-
mal comfort indicators, including the PMV, Thermal Preference Vote
(TPV), and Thermal Sensation Vote (TSV). These indicators are derived
from occupants’ subjective assessments and preferences regarding their
thermal comfort levels. The inclusion of these indicators allows for a
comprehensive analysis of thermal comfort perceptions and preferences.
By incorporating a combination of objective and subjective data,
including measurements and occupants’ feedback, the dataset offers a
holistic view of thermal comfort and IAQ conditions in the buildings
under study. This rich collection of data provides a robust foundation for
analyzing and developing ML-based approaches to enhance thermal
comfort control and optimize energy efciency. Continuous data
collection was carried out using a network of Raspberry Pi boards
embedded with multiple sensors to ensure a comprehensive and pro-
longed assessment of the buildings’ performance. To facilitate this, a
total of 83 Raspberry PIS were deployed to monitor air temperature and
relative humidity, while an additional 21 Raspberry PIS were dedicated
to tracking CO
2
levels.
4.3. Support Vector Machine
Vapnik invented the SVM technique in 1995, based on the notion of
fundamental probability reduction and numerical understanding [43].
The SVM algorithm seeks to minimize the generalization error, which
allows SVMs to simplify extremes even when dealing with unseen data.
SVMs have recently been used in a variety of industries, including
construction, logistics, building thermal comfort, etc. SVR is an exten-
sion of SVMs in which the dataset is translated into a higher dimensional
dataset using a kernel and a set of hyperplanes are tted to optimize the
distance to the dataset (in classication) or to better t the data points
(for regression) and the tted hyperplanes are inversely transformed by
the kernel back to the dataset dimension. This allows the algorithm to
learn nonlinear relationships and outperform algorithms that are limited
to linear or polynomial functions for regression [43].
4.4. Random Forest
RF is an ensemble ML technique based on statistical learning theory
that involves tting a number of decision trees to different subsamples of
the training dataset [44]. The core idea behind the algorithm is to create
a set of trees and use them for prediction through voting. The RF algo-
rithm ow follows a sequence of steps: starting with sample randomi-
zation, followed by feature randomization, constructing m CART
Fig. 3. Building thermal comfort control system overview.
Fig. 4. Examples of buildings where data is collected.
Y. Boutahri and A. Tilioua