Articulo Académico / Academic Paper
Recibido: 02-05-2024 Aprobado tras revisión: 17-06-2024
Forma sugerida de citación: Vallejo, C.; Godoy, F.; Vásquez, F.; Villacreses, G.; Orozco, M.; Navarro, S.; “Analysis and Simulation
of Energy Behavior of Security Buildings in Guayaquil, Ecuador”. Revista Técnica “energía”. No. 21, Issue I, Pp. 133-142
ISSN On-line: 2602-8492 - ISSN Impreso: 1390-5074
Doi: https://doi.org/10.37116/revistaenergia.v21.n1.2024.651
© 2024 Operador Nacional de Electricidad, CENACE
Esta publicación está bajo una licencia internacional Creative Commons Reconocimiento –
No Comercial 4.0
Strategies for Enhancing Energy Efficiency in Public Service Buildings within
a Hot and Humid Climatic Zone: A Case Study in Guayaquil, Ecuador
Estrategias para Mejorar la Eficiencia Energética en Edificios de servicios
públicos, en una Zona Climática Calurosa y Húmeda: Caso de Estudio en
Guayaquil, Ecuador
E.C. Vallejo1
0000-0003-2065-0484
L.F. Godoy1
0000-0002-2878-5175
F.D. Vásquez2
0000-0002-8556-4838
G.P. Villacreses1
0000-0002-0964-0907
M.A. Orozco1
0000-0003-4910-6685
S.I. Navarro1
0009-0002-4881-7485
1Instituto de Investigación Geológico y Energético, Quito, Ecuador
E-mail: catalina.vallejo@geoenergia.gob.ec, felipe_luis_6_d@hotmail.com,
geovanna.villacreses@geoenergia.gob.ec, marco.orozco@geoenergia.gob.ec, santiago.navarro@geoenergia.gob.ec
2University of Connecticut, Storrs, United States
E-mail: francis.vasquez@uconn.edu
Abstract
Since 2010, there has been an approximate annual
increase of 1% in CO2 emissions due to buildings. The
reduction of energy consumption and consequently the
mitigation of Greenhouse Gases are global goals due to
the global issue of climate change. A key step in
achieving these goals is to improve the energy
performance of buildings, thereby reducing energy
consumption and emissions generated by the built
environment. In this regard, the present research
focuses on identifying and estimating strategies to
reduce energy consumption in buildings known as
Community Police Units (UPCs) located in the city of
Guayaquil, which corresponds to a very hot and humid
climate zone. The study consists of statistical analysis
of the historical energy performance of 43 buildings
sharing the same architectural design. Additionally, the
selection of a representative building was made for
energy use monitoring and energy simulation. The
results revealed a potential saving of 16 000 kWh per
year by reducing the control temperature of the air
conditioning system by 1°C and adjusting the on/off
schedule. This is equivalent to 1 150 USD and 5 metric
tons of CO2 emissions.
Resumen
Desde el 2010 existe un incremento aproximado del 1%
anual respecto a las emisiones de CO2 a causa de los
edificios. La disminución del consumo energético y
consecuentemente la mitigación de los Gases de Efecto
Invernadero son metas a nivel mundial debido a la
problemática global del cambio climático. Un paso
clave para alcanzar estas metas es mejorar el desempeño
energético de las edificaciones y de esta manera reducir
la energía consumida y las emisiones generadas por el
parque edificado. En este sentido, la presente
investigación se centra en identificación y estimación de
estrategias para reducir el consumo de energía de los
edificios llamados Unidades de Policía Comunitaria
(UPC) localizadas en la ciudad de Guayaquil, y que
corresponde a una zona climática húmeda muy calurosa.
El estudio está compuesto por el análisis estadístico del
rendimiento energético histórico de 43 edificios, que
comparten el mismo diseño arquitectónico. Además, se
realizó la selección de un edificio representativo para el
monitoreo del uso de energía y simulación energética.
Los resultados evidenciaron un potencial de ahorro de
16 000 kWh al año, al disminuir 1°C la temperatura de
control del sistema de climatización y modificando el
horario de encendido y apagado. Esto es equivalente a
1 150 USD y 5 toneladas equivalentes de CO2.
Index terms— public service buildings, Energy
Efficiency, Energy Savings, Energy Monitoring and
simulation
Palabras clave— Edificios de servicios públicos,
Eficiencia Energética, Ahorro de Energía, Monitoreo y
Simulación Energética.
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Edición No. 21, Issue I, Julio 2024
1. INTRODUCTION
At a global level, in all regions, the increase in
extreme heat events has led to human mortality and
morbidity. These phenomena have been caused by the
expansion of urban areas, the rise in extreme
temperatures, and demographic transformations [1]. It is
estimated that the ambient temperature will increase by
an average of 1.5°C over the next 20 years [2], which
represents an increase in energy demand to achieve
comfort conditions in the buildings.
Ecuador is one of the countries geographically
located in the tropical zone. This geographical zone is
clearly defined as the region located between the tropic
of Cancer and the tropic of Capricorn. A small annual
temperature range is characteristic of the tropics. Near
the equator, the difference in average temperature
between the warmest and coldest months rarely exceeds
4°C, and in some places, it is less than 1°C. Annual
temperature ranges increase as one moves away from the
equator, even at the boundaries of the tropical zone; few
locations have an annual range greater than 15°C [3]. The
world experienced a net temperature increase between
1961 and 2018 of nearly 1°C from the baseline. During
this period, the average warming in the tropics was 0.7°C,
as was the case in South America [4].
This zone is characterized by high temperatures and
high levels of humidity, which are likely to increase with
climate change [5]. Based on current policies, the
installed capacity of cooling equipment globally is
projected to triple between now and 2050, leading to
more than a doubling of electricity consumption [2]. It is
crucial to consider that the refrigerants used in cooling
systems can vary, including CFCs, HCFCs, HFCs,
among others. HFCs, like their predecessors, CFCs and
HCFCs, are predominantly used as refrigerants in air
conditioning and refrigeration equipment. These
substances are potent greenhouse gases. Although HFCs
currently account for around 1% of total global
Greenhouse Gases emissions, their global warming
potential can be hundreds to thousands of times greater
than that of CO2 per unit mass. During the operation,
maintenance and disposal activities of equipment using
these types of refrigerants, leaks are common, directly
contributing to atmospheric warming [6]. In this sense,
there is a need to consider energy efficiency strategies to
decrease the energy consumption of the active cooling
systems without compromising the thermal comfort of
the occupants.
Pickering E, et al [7], analyzed six commercial
buildings and identifies various building characteristics,
including the potential for savings of over 700 MWh
valued from building rescheduling alone, it is shown that
there is an apparent spike in usage at approximately 6h00,
indicating a scheduled Heating, Ventilation, and Air
Conditioning (HVAC) event (i.e. pre-cooling) by the
building management system, followed by a sharp
decrease, showing the tendency for the HVAC units to
overshoot demand and drop in usage. The analysis shows
that the addition of unoccupied set point states would
drastically alter the consumption and save significant
amounts of energy in nighttime/baseload and weekend
operation. This demonstrated the potential impact of no-
cost energy efficiency measures on the energy
consumption of buildings with specific activities.
This research aims to study, identify and estimating
strategies to reduce energy consumption in buildings
known as Community Police Units (UPCs) located in the
city of Guayaquil, which corresponds to a very hot and
humid climate zone. Each UPC has jurisdiction to deploy
police services in a 1 km2 radius area. Currently,
Guayaquil has 104 UPCs; however, the data were
collected from 43 UPCs built in the last four years. They
have similar infrastructure, as well as the same
materiality.
The difference is that some have more glass coverage,
and the area varies according to the services. Therefore,
they were classified as type A and B. The objective of
this research is to establish a baseline on the energy
performance of these buildings and identify the
parameters that affect the thermal behavior and energy
consumption of the UPC.
2. METHODOLOGY
The research is developed under the four-stage
approach:
a) Selection and characterization of the sample
analyzed,
b) Characterization of energy consumption and
analysis of the influence of environmental
temperature on energy consumption
c) Energy simulation.
d) Energy monitoring and identification of
savings opportunities
2.1 Selection and characterization of the sample
For the study, were selected 43 security buildings
built in Guayaquil city in 2014. The selected sample is
located randomly over the city as shown in Fig. 1. The
city is located in a very hot humid climate zone,
considered as an extreme climate zone in Ecuador [8].
According to the Köppen-Gaiger climate classification,
Guayaquil belongs to the Aw group (tropical savannah
climate) [9]. In addition, it has influence of marine
currents from the Pacific Ocean. It is located at 25 m.a.s.l.
with an average temperature of 26 ° C.
UPCs have similar characteristics. They present a
standardized design in their structure and materials.
Although, its difference is that some have a higher
percentage of glass and the area differs according to the
services. Therefore, they were classified as type A and B
(see Fig. 2 Fig. 3). Typology A has a construction area of
225 m2 and typology B of 200 m2. Fig. 4 shows the
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architectural simulated model of the buildings that was
provided by the Works Contracting Service of Ecuador
(SECOB, n.d.).
Figure 1: Location of 43 buildings
Figure 2: UPC Typology A
Figure 3: UPC Typology B
2.2 Characterization of energy consumption and
analysis of the influence of environmental
temperature
The database monthly electricity consumption that
was used corresponds to the years 2016 and 2017, was
provided by the Regulatory Agency for the Control of
Energy and Non-Renewable Natural Resources
(ARCERNNR for its acronym in Spanish).
The energy use intensity EUI was defined from this
information. It is defined as the ratio between the annual
energy consumed and the total construction area [10] as
shown in (1):
𝐸𝑈𝐼 = 𝐸𝑛𝑒𝑟𝑔𝑦 𝑐𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 (𝑘𝑊ℎ)
𝐺𝑟𝑜𝑠𝑠 𝑓𝑙𝑜𝑜𝑟 𝑎𝑟𝑒𝑎 (𝑚2)
(1)
The EUI is considered an indicator of energy use that
allows buildings to be compared. The characterization of
energy consumption in buildings is done through a
frequency distribution analysis of the EUI of the total
sample.
Considering that the energy consumption of the air
conditioning systems is highly influenced by the
environmental temperature [11]. It is necessary to know
the correlation that exists between these factors
(consumption and environmental temperature), in order
to define whether the greatest amount of energy
consumed is due to a base load independent of
temperature (energy related to lighting, office equipment,
etc.) or if there is a high degree of influence of
environmental temperature on the energy consumption of
the building. This analysis allows knowing the energy
consumption of the building depending on seasonal
changes [11].
Figure 4: Architectural model type (a) and (b)
To identify the influence of environmental
temperature on energy consumption, the two variables
were correlated, finding increasing relationships with a
pronounced inclination. The relationship between
ambient temperature and energy consumption was
defined by a linear regression model finding a coefficient
of determination (R2) [12] between 0.4 and 0.7. The
Environmental Protection Agency (EPA) states that the
minimum R2 is 0.4 for simple adjustment ratios and 0.7
for more complex relationships [13]. This analysis allows
identifying in a general way the type of energy efficiency
measures that can be applied to reduce consumption.
(a)
(b)
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Edición No. 21, Issue I, Julio 2024
2.3 Energy simulation
The simulation allows analyzing and determining the
thermal behavior of the building under real
environmental conditions, in order to estimate the
electrical consumption. To carry out the simulation, the
design builder software was used and the following was
considered:
Building design and use: The building is divided into
two floors with independent income. On the ground floor
of the building are: complaint reception area, office, two
bathrooms, warehouse, rack, meeting room, dining room,
laundry, rake and domestic gas room. On the first floor
there are: bedrooms for men and women (only the
women's room has a private bathroom), common
bathroom (with showers) and common area.
Building materials: 150 mm concrete block walls
with internal and external stucco and paint coating, 200
mm concrete slab roof, 6 mm single glass windows and
metal exterior doors with the exception of the main glass
entrance. Thermal properties of the envelope elements
are shown in Table 1.
The weather data for the 2016-2017 period of
Guayaquil city were imported in the format (.epw)
Energy Plus which is compatible with Design Builder.
Table 2. shows the considerations of occupation of the
building. The simulation was calibrated in order to
approximate the model to the real energy consumption
and the conditions of user behavior [14].
2.4 Energy monitoring
To accurately capture data on real electricity
consumption, a comprehensive monitoring system was
installed at the primary distribution panel of the UPC, as
depicted in Fig. 5. The system comprises JANITZA
current transformers, specifically model IPA40.5,
coupled with a JANITZA energy meter, model UMG
103-CBM, and a DEXMA datalogger, model DXHW-
DEXGATE.
Furthermore, ambient temperature measurements
were conducted utilizing a weather station situated within
the socio-housing development 1 in Guayaquil, as
illustrated in Fig. 6. The employed sensor is a VAISALA
model HMP155, meticulously equipped with a solar
radiation shield for optimal precision.
Table 1: Thermal properties of envelopment materials [15], [16]
Element
Material
Thermal
conductivity
[W/m °C]
Density
Specific heat
Solar
absorptance
Emissivity
Thickness
[kg/m³]
[J/kg °C]
[mm]
Roof
Concrete slab
1.4
2 100
840
0.6
0.9
200
Walls
Concrete blocks
0.19
600
1 000
0.6
0.9
150
Stucco [10]
0.72
1 760
840
0.6
0.9
1
Windows
Glass
0.9
-
-
-
0.84
6
Door
Entrance door
0.9
-
-
-
0.84
8
Metal door
50
7 800
450
0.3
0.3
6
Table 2: Building occupation
*Activity
Occupation
(people / m2)
****Environmental control
Computers and
office equipment
Openings
Lighting
Ground
floor
Reception
room and hall
0.04 (24x7
office)
Natural ventilation and Cooling with setpoint:
25 °C
Activate
Sgl Clr 6 mm without
shutter
24x7 Office
ligth
Office
Temperature setpoint cooling:25°C
Activate
Sgl Clr 6 mm with
shutter
Toilets
Mechanic ventilation
N/A
Dinning room
Natural ventilation
N/A
Rack
Temperature setpoint cooling: 25°C
Activate
Meeting room
Natural ventilation
Activate
Laundry
N/A
N/A
Gas room
N/A
N/A
First
floor
Bathrooms
0.0229 **(UPC
Bedroom)
Mechanical and natural ventilation
N/A
Sgl Clr 6 mm with
shutter
Dwell Dom
Bed Ligth
Bedrooms
***Temperature setpoint cooling:26°C
Living room
N/A
Sgl Clr 6 mm without
shutter
* 34 holidays have been considered in Ecuador.,
** A template was created to consider a night-time occupation only.
***This parameter is considered for bedrooms conditioned only at night.
**** It is considered as air conditioning equipment type split no fresh air
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Figure 5: Energy monitoring system
Figure 6: Weather Station
2.5 Identification of savings opportunities
From the findings presented in the preceding sections,
passive strategies were discerned for reduce electricity
consumption, and the associated savings were estimated.
To do this, an electricity rate of 0.072USD/kWh for
community service buildings low voltage [17] and an
emission factor for electricity use of 0.092tCO2/MWh
[18] were chosen.
3. DISCUSSION AND RESULT ANALYSIS
3.1 Characterization of energy consumption and
analysis of the influence of environmental
temperature
Fig. 7 shows the characterization of energy
consumption of the two typologies which was calculated
using (1). It was found that the average EUI in 2016 for
buildings with typologies A and B are 155 kWh / m2 and
129 kWh / m2 respectively, while the average EUI of the
group is 145 kWh / m2. In addition, approximately 30%
of the buildings analyzed have a EUI between 124 and
147 kWh / m2, expected results according to the
methodology defined by [10]. The average values
indicate that type A consumes more energy than type B,
because type A buildings have a higher percentage of
glazing.
On the other hand, the analysis of the EUI, based on
the real consumption of electrical energy in buildings,
determined that consumption decreases in 2017 with
respect to 2016, as is shown in Fig. 8, because of the
environmental temperatures in 2017 were lower than the
year 2016. In addition, if there is no change in the type of
use of the buildings, it is established that the
environmental temperature influences the behavior and
energy consumption.
Figure 7: Characterization of energy consumption
According to Fig. 8, it is defined that buildings that
present EUI greater than 157 kWh / m2, in meteorological
conditions similar to 2017, are categorized as large
energy consumers. Then, the groups of high and medium
consumption are the buildings type A and the buildings
type B have an architectural design which decreases the
energy use. The main architectural differences are that
Type A has a greater amount of glazing and also has 25m2
more construction. On the other hand, Type B has a cover
at the entrance door.
Figure 8: EUI Accumulated frequency analysis to 2016&2017
3.2 Energy simulation
For this research, a building type A was selected,
which was simulated to get its energy behavior. In
addition, it has an altitude of 9 m.a.s.l. which is the
average altitude of the sample. Although, there is no
relationship between the location of each building and the
level of consumption. On the other hand, it is observed
that the environmental temperature influences the energy
consumption of all buildings for the 2016 and 2017
periods.
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Edición No. 21, Issue I, Julio 2024
In Fig. 9 is observed that there is a growing
relationship between electricity consumption and
environmental temperature. The adjustment curve
obtained shows that the energy consumed by the building
presents a greater influence of the temperature from 26°C
with an approximate consumption of 2 000 kWh.
Figure 9: Fitting curve, 24 months of actual energy and weather
data of Building ideal
Subsequently, to validate the results obtained in Fig.
9. The energetic simulation of the ideal building was
carried out with the previously established parameters.
Fig. 10 shows the real energy consumption, simulated
consumption and average environmental temperature for
each month of 2016 and 2017. In addition, the behavior
of cooling degree days (CDD) [15] is included to indicate
the climatic conditions of the place. As a result, for the
period established, it was obtained that the real and
simulated energy behaviors are influenced by the
required cooling loads as a consequence of the
environmental temperature. In addition, a pattern of
consumption is observed every six months which is
repeated in the two years. Where the first semester of the
year presents higher consumption than the second as
shown in Fig. 10.
It is expected that the CDD have a behavior similar to
real and simulated consumption as well as the month of
November 2017, where the three factors decrease at the
same time. The differences between real and simulated
consumption are due to the behavior and operation of the
building itself as its operation and occupation, which
vary during the year according to the dynamics of the city
and climate variability. In the simulation, the occupation
was constant depending on the employees who provide
the public service. Finally, Fig. 10 shows that the basic
consumption of buildings, consisting of the lighting
system and electronic equipment, has a constant value
over 1’000 kWh. The simulated average base
consumption corresponds to 46% of the total
consumption, while for the air conditioning system it is
54% noticing that the energy consumption is related by
the climatic conditions.
It is expected that the CDD have a behavior similar to
real and simulated consumption as well as the month of
November 2017, where the three factors decrease at the
same time. The differences between real and simulated
consumption are due to the behavior and operation of the
building itself as its operation and occupation, which
vary during the year according to the dynamics of the city
and climate variability. In the simulation, the occupation
was constant depending on the employees who provide
the public service. Finally, Fig. 10 shows that the basic
consumption of buildings, consisting of the lighting
system and electronic equipment, has a constant value
over 1’000 kWh. The simulated average base
consumption corresponds to 46% of the total
consumption, while for the air conditioning system it is
54% noticing that the energy consumption is related by
the climatic conditions.
Figure 10: Real consumption, simulation consumption and CDD, results
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3.3 Energy monitoring
To validate the energy simulation, real energy
consumption data of a UPC was collected for April and
May, each 15 minutes. The energy consumption of the air
conditioning systems accounted for 57.4% of the total
building consumption. The remaining 42.6%
corresponds to equipment with energy requirements
independent of external climatic conditions, such as
servers, lighting, printers, and other devices. These
values exhibit an error of less than 5% compared to the
results obtained from the energy simulation.
Using heat maps, the monitored monthly
consumption patterns of the UPC were identified as
shown in Fig. 11. Cool colors (shades of blue or light
blue) indicate low-value ranges, while warm colors
(shades of yellow or red) indicate high values within a
given scale. It is observed that consumption does not
depend on the day of the week but rather on the
occupancy schedule.
The typical consumption schedule during a
workweek is from 9h00 to 10h00, with elevated
consumption observed. The periods of highest
consumption are approximately from 12h00 to 18h00.
Due to the activities carried out in the UPC, the building
is occupied by staff 24 hours a day, 7 days a week. For
this reason, it is not possible to define a specific start or
end time for the workday. However, it is observed that
from 22h00 to 8h00, most equipment, including the air
conditioning, is turned off.
3.4 Identification of savings opportunities
Strategy 1
As shown in Fig. 11, the activation and deactivation
of the air conditioning system are not automatic. The
system is fully activated between 8h00 and 22h00, with
9h00 being the most frequent activation time. The system
is deactivated between 21h00 and 12h00, and in some
cases at 2h00. The building does not exhibit any
difference in energy consumption on weekends
compared to weekdays. However, from 22h00 until
9h00, it shows a similar hourly energy usage pattern
during the week, amounting to 4.5 kWh
In this regard, an energy-saving strategy is proposed,
which involves gradually activating the air conditioning
system starting at 9h00 and deactivating it at 20h00.
These two events must be carried out with the exterior
doors completely closed to control internal thermal load.
When applying the described strategies, a minimum
estimated monthly savings of approximately 9% is
anticipated, equivalent to 10 322 kWh per year, 3.8
metric tons of CO2 emissions [18] and 743 USD
considering the current rate [17].
.
Figure 11: Average real consumption of the building
kWh
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Edición No. 21, Issue I, Julio 2024
Strategy 2
The electrical consumption of the air conditioning
system is highly influenced by external environmental
conditions. This allows for the determination of potential
savings through the adjustment of the minimum
operating temperature of the air conditioning system.
The analysis of Cooling Degree Days (CDD) showed
that 22°C is the minimum activation temperature for the
air conditioning system. By increasing the control
temperature to 23°C, an approximate electricity saving of
16.83% of the system's consumption is expected,
amounting to 478 kWh per month, 413 USD per year
considering the current rate [17], and 1.4 metric tons of
CO2 emissions [18].
In Table 3, the percentage increase or decrease in
monthly consumption of the air conditioning system is
presented when reprogramming the system's operating
temperature. The values presented were determined
based on the consumption data for April and May and
may vary in different seasons with different climate
control requirements.
Table 3: Energy savings percentages based on changes in the on-
off configuration
°C
T20
T21
T22
T23
T24
T20
0.00%
-9.13%
-14.05%
-27.92%
-40.97%
T21
11.97%
0.00%
-14.05%
-21.21%
-35.94%
T22
23.92%
14.05%
0.00%
-16.83%
-32.65%
T23
35.71%
27.92%
16.83%
0.00%
-20.04%
T24
46.81%
40.97%
32.65%
20.04%
0.00%
Table 4 shows the forecasted savings for both
strategies.
Table 4: Results of strategies 1 and 2
Saving
Energy
Emission
Economic
Strategy
1
10 322 kWh
3.8
mtCO2
743 USD
Strategy
2
478 kWh
1.4
mtCO2
413 USD
Total
10 800
kWh
5.2
mtCO2
1 156
USD
4. CONCLUSIONS
The selected sample consists of two types of
buildings A and B, which differ by the construction area
and the amount of glazing. The statistical analysis shows
that the energy consumption of buildings is influenced by
temperature. It has been determined that the year 2016
had a higher temperature than 2017. Furthermore, the
analysis of degree days of refrigeration confirms that
Guayaquil city belongs to the very hot humid zone.
Within 43 buildings, 4 buildings form the group of
high consumption during the two years, which
corresponds to 13% of total energy consumption of the
sample. If the 4 buildings had the energetic behavior of
building type A, an approximate saving of 2 000 USD per
building would be obtained. With this value it is possible
to pay 90% of annual electric service of the monitored
building.
The analyses on the UPC type A shows that the
variables considered in the simulation (envelope,
activity, use, equipment, etc.) are adapted to the real
energy behavior. When UPC uses electrical energy only
for base loads, electricity consumption is not influenced
by weather conditions.
Real information was collected on the disaggregated
energy consumption of a UPC to understand its energy
behavior and in this way identify savings opportunities
and optimize energy use in similar buildings in the same
climatic zone.
Reducing the control temperature of the cooling
system by 1°C has the potential to yield annual savings
of 413 USD. Additionally, optimizing and automating
the activation and deactivation of the cooling system can
lead to savings of 743 USD per year
5. ACKNOWLEDGMENT
This work was fully supported by the Regulation and
Control Agency for Energy and Non-Renewable Natural
Resources (ARCERNNR), Geological and Energy
Research Institute (IIGE), National Police of Ecuador,
and Spanish Agency for International Development
Cooperation (AECID)
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Catalina Vallejo Coral.- She was
born in Quito, Ecuador, in 1986.
She received her degree in
Mechanical Engineering from the
Escuela Politécnica Nacional in
2011, and her Master of Science
with a specialization in Energy
Engineering from Tecnológico de
Monterrey, Mexico, in 2017. Her research fields are
related to energy efficiency, simulation, thermal comfort,
and energy management in buildings and industry.
Currently, she is a researcher at the Instituto de
Investigación Geológico y Energético.
Felipe Godoy.- Originating from
Quito, Ecuador, he holds a
Bachelor's degree in Mechanical
Engineering and is currently
pursuing a Master's in Chemical
and Energy Engineering at Otto-
von-Guericke University
Magdeburg (OVGU). With
experience as a Scientific Researcher at the Instituto de
Investigación Geológico y Energético, he specialized in
energy efficiency in buildings, energy simulations and he
had international project collaboration, with proficiency
in simulation tools, data analysis, and research. Other
experiences include an internship at German Aerospace
Center (DLR), focusing on conceptual models for
electrochemical systems, and as a researcher assistant at
OVGU, conducting thermal simulations and packing
beds. Proficient in MATLAB and Python, they excel in
modeling and analysis, enhancing his expertise in
electrochemical systems and thermal simulations.
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Edición No. 21, Issue I, Julio 2024
Francis Vásquez.- He was born in
Ecuador. He completed his
bachelor’s in Mechanical
Engineering from the Escuela
Politécnica Nacional in 2016 and
his M. Eng in Energy Conversion
Systems and Technologies from the
Rovira i Virgili University in 2020.
He worked for five years as a research assistant at the
Instituto de Investigación Geológico y Energético in
Quito, Ecuador. His main research focus was developing
thermal characterization of phase change materials and
thermal energy simulation in building applications.
Currently, he is a graduate student in the University of
Connecticut working in the Nanoscale Imaging and
Transport (NIT) Lab where develops thermal transport
solutions for micro and nanoscale semiconductor
devices.
Geovanna Villacreses.- She is a
Geographic and Environmental
Engineer, graduated from the
Master of Science in Geographic
Information Systems program at the
University of Salzburg. Since 2014,
she has conducted research in the
field of energy efficiency in
buildings and the application of multi-criteria methods
for site selection with renewable potential in the country.
She is currently working at the “Instituto de Investigacion
Geologico y Energetico” as Director of Innovation.
Marco Orozco.- He was born in
Tulcán and grew up in Quito,
Ecuador. He completed his
undergraduate studies in
Automotive Engineering at the
“Universidad de las Fuerzas
Armadas” and his postgraduate
studies at the “Escuela Politécnica
Nacional” in the Master's program in Design, Production,
and Industrial Automation. He currently works as a
Technical Analyst at the “Instituto de Investigación
Geológico y Energético”. He has experience in the
development of projects related to Energy Efficiency and
Renewable Energies.
Santiago Navarro Vélez.- He was
born in Quito, Ecuador, in 1996. He
obtained his degree in Mechanical
Engineering from the "Escuela
Politécnica Nacional" in 2021.
Currently, he works as a research
assistant at the "Instituto de
Investigación Geológico y
Energético" in the area of Innovation and Scientific
Management. His research focuses on thermal energy
simulation in buildings, thermal comfort and energy
efficiency.
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