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Content may be subject to copyright.
Academic Editor: Stefano Sfarra
Received: 31 October 2024
Revised: 17 January 2025
Accepted: 23 January 2025
Published: 30 January 2025
Citation: Florez-Montes, F.; Martínez-
Lengua, A; Iglesias-Martínez, M.E.;
Giraldo, J.A.T.; Balvis, E.; Peset, F.;
Selvas-Aguilar, R.J.; Castro-Palacio,
J.C.; Monsoriu, J.A.; Fernández de
Córdoba, P. Assessing the Impact of
Thermal Coating Paints on Indoor
Temperature and Energy Efficiency in
Colombian Caribbean Homes. Sensors
2025,25, 842. https://doi.org/
10.3390/s25030842
Copyright: © 2025 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
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(https://creativecommons.org/
licenses/by/4.0/).
Article
Assessing the Impact of Thermal Coating Paints on Indoor
Temperature and Energy Efficiency in Colombian
Caribbean Homes
Frank Florez-Montes 1, Antonio Martínez-Lengua 2, Miguel E. Iglesias-Martínez 3,
John Alexander Taborda Giraldo 4, Eduardo Balvis 5, Fernanda Peset 3, Romeo J. Selvas-Aguilar 6,
Juan Carlos Castro-Palacio 7,* , Juan A. Monsoriu 7and Pedro Fernández de Córdoba 3
1Faculty of Engineering, Universidad de Manizales, Manizales 170003, Colombia; florez.frank1@gmail.com
2School of Exact Sciences and Engineering, Universidad Sergio Arboleda, Santa Marta 470001, Colombia;
antonio.martinezl@usa.edu.co
3Instituto Universitario de Matemática Pura y Aplicada, Universitat Politècnica de València, Camino de Vera
s/n, 46022 Valencia, Spain; miigmar@upv.es (M.E.I.-M.); mpesetm@upvnet.upv.es (F.P.);
pfernandez@mat.upv.es (P.F.d.C.)
4Faculty of Engineering, Universidad del Magdalena, Santa Marta 470003, Colombia;
jtaborda@unimagdalena.edu.co
5Departamento de Ingeniería de Sistemas y Automática, Escuela Superior de Ingeniería Informática,
Universidade de Vigo, Edificio Politécnico s/n, 32004 Ourense, Spain; ebalvis@uvigo.es
6Facultad de Ciencias Físico-Matemáticas, Universidad Autónoma de Nuevo León, Av. Universidad s/n,
Cd. Universitaria, San Nicolás de los Garza 66455, Mexico; romeo.selvasag@uanl.edu.mx
7
Centro de Tecnologías Físicas, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain;
jmonsori@fis.upv.es
*Correspondence: juancas@upvnet.upv.es
Abstract: Thermal coating paints offer a passive strategy to reduce heat gain in buildings,
improve ventilation, and lower energy consumption. This study investigates the effective-
ness of these technologies by comparing different housing structures and environmental
conditions. Specifically, it examines thermal envelope solutions for cool roofs in homes
along the Colombian Caribbean Coast. We quantify the thermal impacts using experimental
data collected from 120 houses across eight municipalities in the Magdalena Department,
Colombia. The research details the technology and analytical methods employed, focusing
on thermal reductions achieved through thermal coatings to potentially reduce energy
demand. A comprehensive measurement system, incorporating temperature and humidity
sensors, is developed to assess the impact of the coatings. Thermal comfort is evaluated
according to the ASHRAE 55 standard, with temperature reductions calculated for each
house treated with thermal coatings. A methodology is applied to evaluate the thermal
reduction between a house with a coating solution versus a house without it. The results
show a temperature reduction on a house-by-house basis, from 1.5% to 16%. On aver-
age, the results yield a significant 7% reduction in thermal load. Additionally, a mobile
application is developed to disseminate the results of this research, promoting the social
appropriation of science among the involved communities.
Keywords: coating paints; buildings; cool roofs; thermal comfort; sensors; open data;
smartphones
Sensors 2025,25, 842 https://doi.org/10.3390/s25030842
Sensors 2025,25, 842 2 of 19
1. Introduction
1.1. Generalities
Regional climates have a significant impact on the energy consumption patterns of a
population. For example, areas characterized by high temperatures and humidity often
experience increased use of ventilation and air conditioning systems, leading to increased
energy consumption [
1
,
2
]. Although this energy consumption varies with the changing
seasons, regions near the equator consistently face high cooling demands [3,4].
Energy consumption in warm regions is intricately linked to comfort requirements
within enclosed spaces, such as residential and office buildings. Consequently, achiev-
ing thermal comfort becomes a primary goal to achieve energy savings. Thermal com-
fort is defined as an individual’s perception of temperature and humidity, among other
factors. Parameters such as temperature, relative humidity, air velocity, clothing types,
and metabolism are recognized factors that affect thermal comfort as described by ISO-7730
and ASHRAE standards [5].
To achieve thermal comfort within enclosed spaces, two main strategies are commonly
employed: active and passive [
6
,
7
]. Active strategies involve the use of air conditioning
systems, which can significantly increase energy consumption. However, through precise
control and technological innovations, it is possible to reduce energy consumption. For ex-
ample, in a study [
8
], it was reported that by implementing a model predictive control
system with adaptive machine learning for the cooling system, significant reductions of
58.5% in cooling energy and 36. 7% in the consumption of air conditioning power were
achieved compared to the original control methods in the case studies.
In recent research, various innovative approaches have been explored to optimize
energy consumption and enhance thermal comfort. For example, the research in refer-
ence [
9
] applied a deep learning algorithm to regulate the energy consumption of fans
and air conditioners in a classroom, effectively managing aspects of comfort, including
thermal comfort and indoor air quality, for 72 occupants. Remarkably, this optimization
resulted in energy savings of approximately 19%, alongside additional health benefits
associated with improved ventilation. Similarly, another study [
10
] utilized predictive
control models to dynamically determine temperature set points in spaces with varying
occupant distributions. This approach led to substantial energy savings, with a maximum
reduction of 21%.
While active strategies like the ones mentioned above are valuable, passive strategies
have also gained prominence in the quest to reduce cooling requirements and maintain
thermal comfort [
11
–
14
]. One such passive strategy is the implementation of green roofs,
photovoltaics, and cool roofs. Green roofs involve the integration of vegetation on exterior
walls and rooftops. A simulation conducted in a commercial building in Cyprus, situated
in the Eastern Mediterranean region, reported heating energy savings ranging from 6% to
13% [15].
Furthermore, various studies on green roofs have yielded different outcomes depend-
ing on geographical location. For instance, research [
16
] conducted on different green
roofs showed variable results based on geographical positions. In warm seasons, cities like
Tenerife experienced thermal reductions between 1% and 11%, Seville between 0% and
11%, and Rome between 2% and 8% [16].
Photovoltaic (PV) roofs and thermal coating solutions are becoming increasingly pop-
ular as strategies for energy efficiency and comfort enhancement in buildings. Photovoltaic
roofs offer numerous advantages, such as local electricity production and shading for
various surfaces [
17
–
19
]. Studies have shown that the success of energy reduction strategies
with photovoltaic panels depends on the size of the PV panel system and the availability of
Sensors 2025,25, 842 3 of 19
roof space. This, in turn, affects thermal reduction, energy production, and carbon emission
savings [20].
Photovoltaic systems are instrumental in achieving Zero Energy Building (ZEB) classi-
fication, signifying a balance between energy consumption and production [
17
]. The po-
tential for ZEB classification by utilizing rooftop photovoltaic systems has been explored,
with considerations for building shape and height. In residential buildings, the use of
solar panels alone has been reported to reduce energy consumption by up to 45% [
20
].
In warm climates, roofs contribute significantly to the cooling load of buildings, mak-
ing cool roofs and coating solutions particularly valuable. Research in regions like the
Colombian Caribbean, the Mediterranean climate zone of Algeria, and other locations has
demonstrated the potential of coating solutions in reducing cooling requirements [17–19].
However, the effect of cool roofs can be challenging to model and quantify as exempli-
fied in [
14
] and [
21
] , where experiments are designed to estimate thermal reductions in
enclosed spaces. Finally, it is necessary to explain that the Colombian Caribbean region is a
territory characterized by high temperatures throughout the year, which leads to a constant
need for energy expenditure in the form of cooling and ventilation systems. The main
objective of this project is to improve the thermal comfort conditions of homes in this region,
using individually and combined passive zoning strategies and renewable energies.
This article focuses on the use of paint coating solutions for homes in warm-climate
regions. The following sections of this article are organized as follows: Section 2describes
the research sites and general information about the project, Section 3presents the results
of the project, Section 4outlines a discussion on the results, and finally, Section 5includes
the main conclusions drawn from this study.
1.2. Project Description
This article presents the analytical and experimental findings from the research project
titled “Investigation of the effects of climate variability and climate change on water resources,
biodiversity, and agricultural activities in Magdalena Department". This collaborative project
involves various public and private organizations from the Colombian Caribbean coast,
with a primary focus on assessing the impact of different zoning technologies on the indoor
conditions of residential properties within the Magdalena Department. The overarching
goal of this research initiative is to provide critical insights and data for informed decision-
making processes related to the mitigation of climate change and the adaptation to climate
variability in the region. The project was thoughtfully organized and executed with the
following key objectives in mind:
•
Contribute to the conservation and recovery of ecosystems with a high potential for
the adaptation and mitigation of climate change.
•
Develop technical conditions for sustainable agricultural and livestock production
through profitable adaptation and mitigation actions.
•
Promote the conservation and recovery of ecosystems with a high potential for climate
change adaptation and mitigation.
• Enhance community resilience by improving living conditions and public health.
The environmental conditions in the Magdalena Department are characterized by
high temperatures and relative humidity, which require the use of ventilation and air
conditioning (HVAC) systems. However, the excessive reliance on these technologies to
improve the thermal conditions of the housingthe housing results in significant energy
consumption. The region faces challenges due to a growing population and increased
energy consumption, particularly in the context of climate change. This increasing de-
mand for energy resources not only strains natural resources, but also imposes continuous
economic costs. Although there is a growing awareness of the urgent need to address
Sensors 2025,25, 842 4 of 19
the impact of high energy costs in buildings, there is a notable lack of concerted efforts
to mitigate these challenges. To address this issue, our research explores two innovative
technologies aimed at improving thermal conditions in various communities within the
Magdalena Department, while minimizing energy consumption: solar roofs and cool roofs.
These technologies involve the installation of photovoltaic systems on rooftops and the
application of a specialized coating solution to the exterior surfaces of residential buildings.
Although some properties benefit from both technologies, this article focuses mainly on
houses equipped with the coating solution.
The project involved 120 households, with an equal number distributed in eight
participating municipalities. These households were selected from a low-income housing
program initiated by the national government, ensuring that all homes adhered to a
standardized design and utilized identical construction materials. Within each municipality,
the sample included five houses treated with thermo-insulating paint, five houses equipped
with both thermo-insulating paint and solar panels, and five control houses that remained
unmodified for comparative analysis.
1.3. Description of the Municipalities and Populations
Figure 1provides a geographical overview of the towns participating in our project,
each designated by a unique flag. Sitio Nuevo is marked with a green flag, Zona Banan-
era with orange, Chibolo with blue, San Ángel with yellow, Platón with brown, Santa
Bárbara de Pinto with gray, and Nueva Granada and Guamal with black and purple
flags, respectively.
Figure 1. Location of the municipalities participating in the project.
Table 1presents more detailed information about the participating communities.
While these communities share geographical and cultural similarities, their population
density provides valuable insights into the characteristics of the municipalities included in
the project.
The selected houses were situated within socioeconomic strata 1 and 2. Researchers
meticulously selected these properties to minimize the impact of external environmental
factors, such as shade cast by trees or the presence of nearby non-residential structures.
Each house, regardless of whether it received any zoning technologies, was outfitted with a
comprehensive system for monitoring environmental conditions.
Sensors 2025,25, 842 5 of 19
The installation of these zoning and metering technologies began in December 2019,
and culminated in June 2021, although data collection continued until October 2021.
The coating solution is a commercial product, protected by patent secrets; however,
the available physical properties are shown in Table 2[22].
Table 1. Information about the municipalities included in this research.
Municipality Surface (km2) Population Geographical Coordinates
Guamal 554 25,058 9◦08′39′′ N 74◦13′25′′ O
Plato 1500 48,898 9◦47′33′′ N 74◦46′57′ ′ O
Nueva granada 843 16,088 9◦48′04′′ N 74◦23′30′′ O
Santa Barbara de Pinto 497 12,610 9◦26′07′′ N 74◦42′06′′ O
Nuevo 967 26,777 10◦46′33′′ N 74◦43′13′′ O
Zona Bananera 443 4944 10◦45′51′′ N 74◦09′26′ ′ O
Chibolo 527 16,018 10◦01′35′′ N 74◦37′17′′ O
Sabanas de San Ángel 1196 11,425 10◦13′25′′ N 74◦12′56′′ O
Table 2. Physical properties of the coating solution.
Physical Property Value Unit
Thermal conductivity 0.0556 W/(m·K)
Density 485.4 kg/m3
Thickness (recommended) 0.5 mm
UV refractive percentage 82 %
Brookfield Viscosity 38,200 Cp
2. Materials and Methods
2.1. Technology Implementation
To assess the impact of zoning technologies, we developed a monitoring system
to record indoor temperature and humidity, as well as outdoor ambient temperature
for each house. Figure 2illustrates the conceptual design of this measurement system,
which comprises a microcontroller, memory unit, internal battery, geographic positioning
modules, environmental sensors, and transmission components. The system is powered
by a 12 V adapter and utilizes a quadband modem to establish a GPRS/GSM connection,
ensuring compatibility with Colombian mobile operators and supporting the following
frequency spectrum: 850/900/1800/1900 MHz. Table 3includes the key features of the
designed datalogger.
The core of the recording system is a Raspberry Pi 3B+ card, which operates continu-
ously whenever power is available. In cases where power is interrupted, the recorded data
are temporarily stored in the internal memory and transmitted when power is restored.
Figure 3illustrates the project’s PCB, designed and fabricated. The left panel presents a 3D
rendering of the board, while the right panel showcases the assembled board undergoing
initial testing. Altium Designer was employed for the PCB design.
Sensors 2025,25, 842 6 of 19
Figure 2. Conceptual design for environmental measurement system.
Table 3. Key features of the designed datalogger.
Parameter Description
Operation voltage 12 VDC
Energy consumption 0.5 W
Sensors Up to 4 humidity and temperature sensors
Sampling rate Every 1 h
Shipping frequency Every 2 h
External memory 256 MB (1 million 200 thousand measurements approx.)
Communication GPRS/GSM
Sensibility −106 dBm
Bands 850/900/1800/1900 MHZ
Figure 3. Design and implementation of the recording system. Design in the left image and prototype
built in the right image.
The system supports multiple temperature and humidity sensors and is enclosed in
a protective plastic box, which also houses the power adapter, internal memory, clock,
and battery. Four temperature and humidity sensors are connected to the cards, and data
are sampled at a frequency of one hour. The collected data are stored in both RAM and
external memory, and information is transmitted to the server every two hours. In Figure 4,
you can see the recording system along with the sensors and the protective box. A total of
120 logging systems were constructed, with one for each participating house.
Sensors 2025,25, 842 7 of 19
The sensors used are SHT20 sensors, which include both a temperature and a humidity
sensor. The humidity measurement technology is capacitive, while temperature is measured
using bandgap technology. These sensors communicate with the datalogger via the I2C
interface, allowing for cable lengths of up to 10 m between the datalogger and the sensors.
Inside each house, two SHT20 sensors are installed, while two additional sensors are placed
outside and protected by a weather shield, as shown in Figure 5.
Figure 4. Cards and sensors for installation, image on the left with a fully equipped card ready for
installation. The image on the right shows a card bank without sensors.
Figure 5. Shield for exterior sensors and their possible position on the roof tile.
2.2. Datalogger Operation
The datalogger is designed for low energy consumption and operates in a standby state
until it initiates the process of data acquisition, verification, and transmission. The operation
of the datalogger device can be summarized in the following stages:
•
Acquisition and digitizing: At the specified time, the datalogger sends a command to
the humidity and temperature sensors to initiate a new reading.
•
Reading: The datalogger communicates with the sensors to collect the measurements.
•
Verification: The datalogger validates the obtained measurements, discarding erro-
neous data, and repeating the reading process if necessary.
•
Storage: The data are stored in both external memory and integrated RAM. Following
storage, the datalogger returns to the standby state.
•
Sending: At the designated time, the datalogger activates the GPRS/GSM modem,
prepares the measurements, calculates the CRC, and transmits the data to a server. It
then returns to the standby state. If data transmission fails, the data remain stored
until a new attempt is made.
The steps described above are summarized in Figure 6.
Sensors 2025,25, 842 8 of 19
Figure 6. Block diagram of datalogger operation.
2.3. Research Data Management
The data generated during the research are of two types: (i) data verified with a
datalogger, which cleans the raw data coming directly from the sensors installed in the
houses, both with and without coating, and the (ii) calculated data. The raw data are not
stored or made available, as they contain errors that could bias subsequent research. Due
to their ‘dirty’ nature, they are discarded to prevent the experiment from being reproduced
with errors.
Both types of data, (i) and (ii), are protected under intellectual property laws. Specifi-
cally, the ‘sui generis’ right applicable to databases with manipulated data grants the rights
holder the ability to release them or not. Since these are research data, the principle of the
European Union, “as open as possible, as closed as necessary”, is followed [23].
Given this research commitment to scientific advancement, some data will be made
available in the future once their full potential has been exploited, in order to comply
with the FAIR principles (findable, accessible, interoperable, and reusable), as well as
Spanish and Colombian regulations. To achieve this, the data will be deposited in a
repository that accepts such data, establishing reciprocal links from the articles to the
databases. The institutional repository of Universitat Politècnica de València, RiuNet
http://riunet.upv.es/, and a general repository, ZENODO http://zenodo.org/, are planned
to be used. In addition, a mobile application has been developed to disseminate the project’s
results, fostering the social appropriation of science within the involved communities.
2.4. Methodology
The effectiveness of the coating solution is evaluated by analyzing temperature data.
A single house within a municipality, featuring the cladding solution applied to its exterior
surfaces, is compared to all other houses in that municipality that do not utilize zoning
technology. Temperature comparisons are performed individually between the house
with the coating and each of the houses without cladding. The percentage temperature
difference is calculated for each comparison. Subsequently, the average of these individual
percentage differences is calculated to quantify the overall thermal reduction achieved by
the coating solution compared to the other houses in the municipality. This methodology is
adopted to mitigate the influence of individual house-specific factors. For instance, in some
locations, natural elements such as trees provide shade, reducing the interior temperature,
while in other houses, roofing or wall materials may lead to higher internal temperatures.
Figure 7illustrates the correlation between the houses established by the methodology.
Sensors 2025,25, 842 9 of 19
Figure 7. Comparison methodology.
To accurately assess the thermal reduction achieved by a coating solution, it is essential
to compare the internal temperature of the treated house to a control house without any
modifications. Comparing to a house with inherently high internal temperatures, such
as a poorly insulated house or one with excessive solar gain, would artificially inflate
the perceived thermal reduction provided by the coating. In contrast, comparing to a
house with existing shading elements, such as trees or awnings, could mask the true
effectiveness of the coating solution, as these elements already contribute to reduced
internal temperatures.
The methodology employed ensures a realistic assessment of the impact of the coating
solution on a house when compared to all others. To calculate the thermal reduction (
TR
),
we use Equation (1), where Tcs represents the internal temperature of a house with a coating
solution, and Ts represents the internal temperature of a house without the solution:
TR =q∑(Tcs −Ts)2
q∑(Ts)2
×100% (1)
In addition, we can have as a means of improvement the possible use of artificial
intelligence tools and the wavelet transform. These elements could be considered for
refinement of the initial data and the use of AI to fill in missing data. This approach will be
explored in future research aimed at achieving a more accurate and efficient comparison,
ultimately leading to results of higher quality and scientific relevance.
Finally, to guarantee the comfort sensation, the ASHRAE 55 standard comfort psychro-
metric chart is used.These graphics illustrate the concentration and variation in temperature
measurements [5,24].
The ASHRAE 55 standard provides a framework for determining the satisfactory
relationship between humidity and temperature [
5
,
25
]. The comfort zone shape is a
parallelogram, defined by calculating the coordinates specified in Figure 8.
Sensors 2025,25, 842 10 of 19
10 15 20 25 30 35 40
Temperature[°C]
0
2
4
6
8
10
12
14
Humidity [g/Kg]
P1
P2
P3
P4
Figure 8. Comfort zone defined for this region.
The points
P1
and
P2
are determined using Equations (2) and
(3)
, respectively. The ther-
mal insulation provided by the clothing (clo) is set at 0.57, representing typical clothing for
the region, specifically, pants and a short-sleeved shirt. The representative temperatures for
the region are selected as follows:
TP1−sup =
26 °C,
TP1−in f =
24 °C,
TP2−sup =
23 °C and
TP2−in f =20 °C. We have the following:
P1=(clo −0.5)∗TP1−in f + (1−clo)TP1−sup
0.5 (2)
P2=(clo −0.5)∗TP2−in f + (1−clo)TP2−sup
0.5 (3)
Points
P3
and
P4
are determined using Equations (4) and
(5)
. The constant
Im
, as de-
fined by the standard, is set to 0.43. The Lewis Ratio constant (
LR
) is
LR =21.7054 ◦C/hPa
,
and the skin moisture (w) is approximated as w=0.03:
P3=P1−12 ∗w∗Im ∗LR (4)
P4=P2−12 ∗w∗Im ∗LR (5)
3. Results
The first result obtained in the project is the construction of a database with environ-
mental and internal data of the houses. The environmental data are studied below.
The geographical proximity of Colombia to the Equator results in challenges for
nationalizing processes across its territory. The Magdalena Department, located along the
coast, is susceptible to meteorological phenomena such as typhoons or hurricanes. Typically,
daytime temperatures average above 30
◦
C, with nighttime temperatures approaching
25
◦
C. However, daily averages can exhibit significant variations. In our research, we
observe daily averages ranging from 31.3
◦
C to 26.7
◦
C within the same month. These
variations can fluctuate by up to 15% in daily means, making it challenging to analyze the
same house over time on different days.
Figures 9and 10 contain the ambient temperature data collected during the entire
project for all houses and municipalities, where the sensors are distributed as follows:
• s1: Sitio Nuevo →591 registers;
• s2: Zona Bananera →679 registers;
• s3: Sabanas San Angel →188 registers;
• s4: Chibolo →199 registers;
• s5: Santa barbara de pinto →931 registers;
Sensors 2025,25, 842 11 of 19
• s6: Nueva Granada →1067 registers;
• s7: Guamal →876 registers;
• s8: Plato →1128 registers.
Figure 7demonstrates that temperature across the majority of municipalities exhibits
a concentrated distribution between 28 °C and 35 °C, with a median value approximating
30 °C. The dataset exhibits a limited number of observations exceeding 25 °C or falling
below 40 °C. In a similar vein, Figure 8illustrates that humidity levels across the munici-
palities primarily fall within the 65–85% range, with median values centered around 75%.
Nevertheless, instances with humidity exceeding 90% or falling below 50% are observed.
These visual representations suggest the presence of potential outliers within the data,
necessitating a data preprocessing stage to ensure data quality and accuracy.
Another challenge encountered with the data is the disparity in the number of records
collected from each municipality. This is mainly caused by power outages, which are
common in some areas and hinder direct comparison. As a result, temperature and hu-
midity were not recorded simultaneously in all homes and municipalities on the same
days. To address this issue, a methodology for analyzing the data is proposed. How-
ever, the recorded experimental data agree with the theoretical information about the
environmental conditions of the region.
Figure 9. Environmental temperature in all municipalities.
Figure 10. Environmental humidity in all municipalities.
To mitigate errors caused by weather variability, estimates and comparisons are
conducted on the same day to ensure consistent exposure to environmental conditions
for all houses. Before comparing the results for the different houses, it is verified that the
recorded environmental conditions are not statistically different. A Student’s
t
-distribution
Sensors 2025,25, 842 12 of 19
is used for this purpose, generating a
p
-value of 0.03, which leads to the rejection of the
hypothesis that the environmental data are different.
Figures 11–18 illustrate the stark differences from one day to the next within the same
month. Each figure depicts indoor temperatures in different houses on various days in June
2021, with each figure corresponding to a specific city. In each case, a house with a thermal
coating solution is compared to houses without any zoning technology. The blue line in
each figure represents the temperature of a house with the thermal solution. The effects of
the thermal coating solution are calculated by comparing painted and unpainted houses,
subject to the following conditions:
•
Temperature reductions are considered only during the daytime period (from 6:00 to
18:00).
•
Comparisons are made if there are temperature records for the same day in both
houses.
• There must be more than 20 records in a day to enable a comparison.
• Outliers must be fewer than 3 in a single day for comparison.
• For comparison, two data points must be taken less than one hour apart.
Figure 11. Internal temperature in houses 4, 6 (painted), 9, and 8.
Figure 12. Internal temperature in houses 12 (painted), 15, and 19.
Sensors 2025,25, 842 13 of 19
Figure 13. Internal temperature in houses 22, 23 (painted), and 27.
The constraints established for this project are essential for addressing the various
challenges encountered during data collection and analysis. The database presents a non-
uniform structure, characterized by missing data points and inconsistencies in the time
series due to frequent power outages. These power outages, a common occurrence in this
geographic region, lead to sudden interruptions in data collection, resulting in erroneous
data points. These outliers are identified and subsequently removed during the initial
stages of data analysis.
Figure 14. Internal temperature in houses 5, 25 (painted), 35, and 42.
Figure 15. Internal temperature in houses 30 (painted), 33, and 34.
Sensors 2025,25, 842 14 of 19
Figure 16. Internal temperature in houses 39 (painted), 54, 55, and 56.
Figure 17. Internal temperature in houses 41 (painted), 71, 90, and 91.
Figure 18. Internal temperature in houses 0, 64 (painted), 79 and 70.
The primary objective of the project is to assess the impact of the passive zoning
technology on indoor temperatures in residential homes. This article specifically focuses
on coating solutions. However, indoor temperature is just one aspect of thermal comfort,
which can be evaluated through humidity and temperature measurements. Figure 19
displays a psychrometric chart with a comfort zone for two sample villages, Sabanas de
San Ángel and Santa Barbara de Pinto, with black and red dots representing temperature
and humidity measurements in houses with and without coating solutions, respectively.
Sensors 2025,25, 842 15 of 19
20 25 30 35 40 45 50 55
Temperature[°C]
0
5
10
15
20
25
30
35
40
45
50
Humidity [g/Kg]
Sabanas de San Angel
20 25 30 35 40 45 50 55
Temperature[°C]
0
5
10
15
20
25
30
35
40
45
50
Humidity [g/Kg]
Santa Barbara de Pinto
Figure 19. Psychrometric chart for Sabanas de San Ángel and Santa Barbara de Pinto.
Under natural ambient conditions, none of the houses fall within this comfort zone.
However, homes with coating solutions are closer to achieving it, indicating that they
would require less energy from HVAC systems to reach the comfort zone. Notably, houses
with coating solutions (black dots) show lower maximum internal temperatures compared
to those without (red dots). This implies reduced cooling requirements for houses equipped
with zoning technology. Figure 20 displays temperature and humidity measurements for
all houses across different cities. In this figure, you can observe that measurements for
houses with coating solutions (black dots) are mostly within the range of 25 to 40
◦
C), while
those without (red dots) exhibit a wider dispersion, sometimes exceeding 50 ◦C).
-10 -5 0 5 10 15 20 25 30 35 40 45 50 55
Temperature[°C]
0
10
20
30
40
50
Humidity [g/Kg]
Houses with coated solution
Houses without coated solution
Figure 20. Psychrometric chart for all towns.
4. Discussion
Temperature reductions are considered indicative of cooling energy savings, as they
lead to the reduced usage of ventilation and cooling systems. The savings for each house
are calculated by averaging the temperature reductions across unpainted houses.
Figure 21 illustrates the savings for each house in the study. The blue dots represent
the calculated averages, while the yellow line represents the thermal reduction range of the
Sensors 2025,25, 842 16 of 19
comparison. This methodology prevents the overestimation of results. For example, house
number 96 achieved reductions of 16% and 1.5%. Focusing on a single value would not
accurately reflect the overall impact of the coating solution. The average energy savings for
all the houses in the study amount to 7.19%.
Figure 21. Environmental temperature in all municipalities
These results are consistent with previous estimates and research. In the [
26
] research,
savings range from 2.1% to 9.13%. In another research on solar roofs [
27
], the energy
savings due to temperature reduction reach 17%. In a research study on cool roofs in
Australia, the savings are as high as 8% in residential buildings [
28
]. All these results are
in line with our estimate of 7.19%, and are very close to the calculations developed in our
own previous research [
29
–
31
]. It is important to note that while coating solutions offer
benefits, they may not entirely replace the need for ventilation and air conditioning systems
but can effectively reduce their usage. Furthermore, the thermal reductions achieved can
be enhanced when combined with other zoning technologies such as green roofs and
photovoltaic systems
[32–34]
. In future research, it will be valuable to analyze houses
equipped with both coating solutions and photovoltaic panels. This analysis should also
consider the energy production from photovoltaic systems and their potential economic
advantages for the community.
5. Conclusions
Addressing the challenges of climate change is a formidable undertaking. To tackle this
issue, a pilot project was initiated in Magdalena Department, employing coating solution
paints and photovoltaic systems to reduce cooling requirements in diverse communities
across the region. This pilot initiative, titled ’Investigation of the effects of climate variability
and climate change on water resources, biodiversity, and agricultural activities in Mag-
dalena Department’, encompasses a range of communities, including Sitio Nuevo, Zona
Bananera, Chibolo, San Angel, Plato, Santa Barbara de Pinto, Nueva Granada, and Guamal.
Throughout the project’s implementation, a total of 120 measurement cards were man-
ufactured and installed in various households within the participating communities. These
measurement cards were equipped with SHT20 sensors to collect data on temperature and
humidity. Some households were equipped with both the coating solution and photovoltaic
systems, while others received only one of these technologies. This article primarily focuses
on assessing the impact of the coating solution.
The importance of passive strategies, such as the use of thermal envelopes, is high-
lighted as a viable solution to improve thermal comfort and reduce energy consumption in
Sensors 2025,25, 842 17 of 19
low-income housing. These strategies are especially relevant in contexts where resources
for active solutions are limited.
The research demonstrates that zoning technologies have a significant effect on reduc-
ing internal temperatures in homes. When comparing houses with and without coating, it
was observed that the former were able to maintain lower internal temperatures, which not
only improves thermal comfort but also reduces the need for cooling systems, contributing
to lower energy consumption.
The collected temperature and humidity data were evaluated using the ASHRAE
Standard 55 and presented on a psychrometric chart. The analysis revealed that houses
with the coating solution consistently maintained maximum temperatures within the range
of 40 °C to 45 °C. In contrast, houses without the coating solution experienced maximum
temperatures ranging from 50 °C to 55 °C.
To quantify the energy savings related to cooling, the Euclidean norm was employed
for each house. The results showed that, on average, houses with the coating solution
achieved energy savings ranging from 2.5% to 14%. When considering all houses, the aver-
age energy savings amounted to 7.19%.
Despite the difficulties encountered, such as power outages and data irregularities,
the methodology employed for data analysis was effective. This highlights the need for
robust and adaptable monitoring systems in studies of this type, which can serve as a
model for future research in other regions.
Additionally, it is worth noting that combining coating solutions with photovoltaic
systems has the potential to further enhance energy savings. Given the region’s abundant
solar radiation, which intensifies cooling related energy consumption, future research
endeavors should delve into estimating energy savings from individual photovoltaic
panels and combined passive zoning strategies.
Author Contributions: Conceptualization, F.F.-M., M.E.I.-M. and P.F.d.C.; methodology, A.M.-L., F.P.,
R.J.S.-A. and E.B.; software, A.M.-L., J.A.T.G. and J.C.C.-P.; validation, M.E.I.-M., F.P. and P.F.d.C.;
formal analysis, F.F.-M., J.A.M. and A.M.-L.; investigation, F.F.-M., J.A.M. and J.A.T.G.; resources,
P.F.d.C., F.P., J.C.C.-P., J.A.M. and M.E.I.-M.; data curation, F.F.-M., F.P. and A.M.-L.; writing—original
draft preparation, F.F.-M., M.E.I.-M. and P.F.d.C.; writing—review and editing, F.F.-M., F.P., M.E.I.-M.,
J.C.C.-P. and P.F.d.C.; visualization, F.F.-M.; supervision, P.F.d.C. All authors have read and agreed to
the published version of the manuscript.
Funding: This study is part of the results derived from the project “Investigacion de los efectos de
la variabilidad climatica y el cambio climatico sobre el recurso hidrico, biodiversidad y actividades
agropecuarias en el Departamento del Magdalena”, funded by Fondo de CTeI (Sistema General de
Regalias, SGR)—MinCiencias (Colombia). Miguel E. Iglesias Martinez’s work was partially supported
by the postdoctoral research scholarship “Ayudas para la recualificacion del sistema universitario
espanol 2021–2023. Modalidad: Margarita Salas” from the Ministerio de Universidades, Spain, funded
by the European Union Next Generation EU. Fernanda Peset works under the umbrella of the grant
“Stable methodologies to evaluate and measure quality, interoperability, blockchain and reuse of open
data in the agricultural field” PID2019-105708RB-C21 funded by MCIN/AEI/10.13039/501100011033.
Pedro Fernandez de Cordoba’s work was partially supported by the Spanish government through the
R&D projects PID2021-128676OB-I00 and PID2022-142407NBI00, as well as by the project “Foresight
and futures analysis of agricultural products from the Huerta of the Valencia Region”, funded by the
Excellence Research Groups Program of the Generalitat Valenciana, PROMETEO CIPROM/2023/32.
Juan Carlos Castro Palacio and Juan A. Monsoriu were partially supported by the Spanish government
through the R&D project PID2022-142407NB-I00.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Sensors 2025,25, 842 18 of 19
Data Availability Statement: The data that support the findings will be available in RiuNet or
Zenodo at http://riunet.upv.es/ or http://zenodo.org/ following an embargo from the date of
publication to allow for commercialization of research findings.
Acknowledgments: The authors would like to express their sincere gratitude to all individuals and
organizations that contributed to the successful completion of this research.
Conflicts of Interest: The authors declare no conflicts of interest.
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