Novasinergia 2022 5(1), 132-157. https://doi.org/10.37135/ns.01.10.08 http://novasinergia.unach.edu.ec
Research article
Microclimate influence in buildings thermal comfort and energy performance: A
numerical assessment of a historical heritage settlement
Influencia del microclima en el confort térmico y desempeño energético de las
edificaciones: Evaluación numérica del asentamiento de un patrimonio histórico
Katherine Rodríguez Maure1, Dafni Mora1,2 , Miguel Chen Austin1,2,*
1 Facultad de Ingeniería Mecánica, Universidad Tecnológica de Panamá, Ciudad de Panamá, Panamá, 0819-07289;
dafni.mora@utp.ac.pa
2 Ingeniería y Tecnología, Centro de Estudios Multidisciplinario en Ciencias, Ciudad de Panamá, Panamá; 0819-07289;
katherine.rodriguez8@utp.ac.pa
*Correspondencia: miguel.chen@utp.ac.pa
Citación: Rodriguez, K., Mora, D., &
Chen, M., (2022). Microclimate
influence in buildings thermal
comfort and energy performance: A
numerical assessment of a historical
heritage settlement. Novasinergia. 5(2).
132-157.
https://doi.org/10.37135/ns.01.10.08
Recibido: 20 septiembre 2021
Aceptado: 07 febrero 2022
Publicación: 05 julio 2022
Novasinergia
ISSN: 2631-2654
Abstract: Due to climate change consequences, microclimate effects, and heat
islands in cities, this research seeks to evaluate the natural potential ventilation
and energy use at an urban scale in the Casco Antiguo of Panama City. The
Natural Ventilation Climate Potential (NVCP), the Natural Ventilation
Satisfied Hours (NVSH), and the Electricity Consumption Indicator (ECI)
were used as indicators to evaluate natural ventilation. For this purpose, a
methodology based on dynamic energy simulations was conducted by
coupling ENVI-met and DesignBuilder software. Furthermore, an
optimization process based on parametric studies, sensitivity, and uncertainty
analysis allow for comparing envelopes of construction materials, glazing, and
operating hours of windows with thermal discomfort hours. Results showed
that the area under study does not have a beneficial micro-climate for natural
ventilation (NVCP = 19.41%), affecting indoor thermal comfort (NVSH =
3.11%). Finally, an optimization process showed that envelope materials do
not generate significant changes concerning hours of thermal discomfort in
passive mode at the urban scale. However, energy use in active mode was
reflected by implementing walls and roofs of superinsulated material.
Keywords: Dynamic energy simulation, energy consumption, microclimate,
natural ventilation, thermal comfort.
Copyright: 2022 derechos otorgados
por los autores a Novasinergia.
Este es un artículo de acceso abierto
distribuido bajo los términos y
condiciones de una licencia de
Creative Commons Attribution (CC
BY NC).
(http://creativecommons.org/licenses/
by/4.0/).
Resumen: Debido a las consecuencias del cambio climático, las afectaciones del
microclima y el efecto de isla calor en las ciudades, esta investigación busca evaluar el
potencial de ventilación natural y aprovechamiento energético a escala urbana en
Casco Antiguo de la Ciudad de Panamá. Indicadores como Potencial Climático de
Ventilación Natural (PCVN), Horas Satisfechas de Ventilación Natural (NVSH) e
Indicador de Consumo Eléctrico (ICE) fueron empleados. Para ello, se llevaron a cabo
simulaciones energéticas dinámicas acoplando los programas ENVI-met y
DesignBuilder. Un proceso de optimización a partir de estudios paramétricos y
análisis de sensibilidad permite comparar materiales de construcción de envolventes,
acristalamientos y horario de operación de ventanas con cantidad de horas de
incomodidad térmica. Los resultados mostraron que el espacio construido no cuenta
con un microclima beneficioso para ventilación natural (PCVN = 19.41%) y esta no
favorece el confort térmico interior de las edificaciones (NVSH = 3.11%). El proceso
de optimización mostró que los materiales de las envolventes no generan cambios
significativos con respecto a horas de incomodidad térmica en modo pasivo a escala
urbana; sin embargo, un aprovechamiento energético en modo activo se vio reflejado
al implementar paredes y techos de material super aislado.
Palabras clave: Confort térmico, consumo energético, microclima, simulación
energética dinámica, ventilación natural.
Novasinergia 2022, 5(2), 132-157 133
1. Introduction
It is important to understand the process that must be carried out for adequate
natural ventilation in buildings’ design, adaptable to urban areas’ scenarios. The analysis at
an urban scale can be achieved through simulations with tools that allow evaluating the
wind behavior (Yi & Peng, 2014). Since 2000, this new technique that combines energy
simulation with optimization has started an emerging technology that generates new
designs based on computational modeling results. This efficient methodology guarantees
an optimal design, providing solutions to several problems (Lin, 2014). Yi & Peng (2014)
proposed coupled outdoor-indoor microclimate simulation for the passive design of urban-
scale buildings. The authors extracted an urbanization’s microclimate condition through
simulations in ENVI-met software, allowing the visualization of wind direction and areas
with higher temperatures. It was observed that the wind speed around the building is
drastically reduced, which could impact the interior thermal conditions of the building. The
authors emphasize that the indoor thermal performance interacts with specific outdoor
microclimates, thus showing the importance of a coupled indoor-outdoor simulation at the
microclimate level to implement passive design features in buildings.
On the other hand, Jiang, Wu, & Teng (2020) used ENVI-met software to simulate wind
parameters in six case studies based on the various spatial distribution of a set of buildings.
The characteristics of microclimatic conditions were compared and evaluated in terms of
wind and air temperature. This study highlights the relationship between the distribution
of buildings and wind in an outdoor environment. According to the direction of the
prevailing wind, a correct arrangement of the buildings allows the formation of a ventilation
corridor that favors airflow, thus preventing the structures from causing a barrier effect. The
authors indicate that the hypodermic layout should be adequate by allowing the insertion
of wind into the constructions and facades of the buildings.
The study by Tong, Chen, & Malkawi (2016) was based on the configuration of surrounding
urbanization and the airflow through naturally ventilated buildings. Most of the studies
have revealed that airflow is extremely sensitive. Different variables of urban morphology
are an axis of action in this matter. The authors refer to the coupled exterior-interior
technique they achieve using computational fluid dynamics (CFD) simulations. The authors
stated that key urban parameters such as wind condition (wind direction and speed), H/W
aspect ratio (building height/street width), the height of the buildings relative to the
surroundings, and the number of obstacles in the direction of the wind flow are influential.
The analysis of their work showed the influence of the surrounding building layers on the
building under study. They also state that wind direction is significant for the flow pattern
through and around the buildings. They point out that the flow field around the buildings
is more “aerodynamic” under an oblique wind direction (45°); thus, the air change rate per
hour was noticeably higher.
On the other hand, Kusumastuty, Poerbo, & Koerniawan (2018) considered building
configurations, such as distance between structures, average height, orientation, and road
width, to analyze a study based on the principle of climate-sensitive urban design.
Novasinergia 2022, 5(2), 132-157 134
Bouchahm, Bourbia, & Bouketta (2012) performed a numerical simulation to evaluate the
effect of wind, considering parameters such as orientation, the geometry of buildings,
spatial arrangement, angle of wind incidence, and vegetation. Similarly, Priyadarsini, Hien,
& Wai (2008) performed a numerical study of air temperature under different wind speed
levels, noting that the air temperature decreases with increasing speed. Meiss & Feijo (2017)
presented the use of a model capable of qualifying and quantifying the available natural
ventilation flows applied to energy retrofitting of residential districts. It considered factors
such as wind pressure, building envelopes, and turbulence.
At the Faculty of Built Environment, University of New South Wales, Sydney, a study on
natural ventilation performance and its associated influence on outdoor thermal
environment potential and thermal comfort was conducted (He, Ding, & Prasad, 2020).
Some mitigating measures for the local heating caused by the urban heat island (UHI)
phenomenon were presented. Through urban planning and design techniques, the insertion
of wind at lower temperatures was allowed, thus mitigating the UHI. This is one of the main
reasons causing the reduction of natural ventilation.
At the City University of Hong Kong, a study was conducted to examine the influence of
urban canyon aspect ratio and tree configuration on microclimate and thermal comfort
(Morakinyo & Lam, 2016). This study included the relationship between tree height and
crown diameter, trunk height, leaf area index, and planting pattern, subject to different wind
conditions. It was found that there was some reduction in air temperature and the mean
radiant temperature. The latter was dependent on radiative fluxes (specifically direct
shortwave radiation). The trees’ shading effect is attributed to these changes in the
microclimate and thermal comfort of the urban canyon.
The evaluation of the modeling techniques previously implemented by researchers
contributes to analyzing the parameters and indicators that should be considered for
studying natural ventilation potential at the urban scale. One of the approaches to determine
the indicators at the design stage is the coupled dynamic simulation. At this stage, objective
criteria can be selected to evaluate the performance of the indicators often used in the
literature.
Providing a better understanding of the surroundings’ microclimate before applying
natural ventilation to the design is a fundamental step in mitigating the consumption cost
in a building from the beginning of its conception (Pérez, 2018). Furthermore, it is possible
to take advantage of this natural resource to obtain good thermal comfort and ensure
optimal indoor air quality (Kastillo & Beltrán, 2015). The use of natural ventilation strategy
could achieve energy savings of 9.7%, especially for residential buildings (Guía de
construcción sostenible para el ahorro de energía en edificaciones, 2016). A simulation study
in a residential building area was performed to study the effectiveness of natural ventilation;
Amin Mirakhorli modeled in EnergyPlus building thermal behavior (Mirakhorli, 2017).
Currently, there is significant uncertainty about the future energy sustainability of Panama.
The growing urbanization of Panamanian society has led to a massive construction method
over time, leaving aside indoor comfort parameters, which leads users to opt for
Novasinergia 2022, 5(2), 132-157 135
electromechanical air conditioning systems that considerably increase energy consumption
(Pérez, 2018). Statistics show that almost 60% of the electricity consumption in Panama is
concentrated in providing comfort to stores, residences, and public and private offices,
where 11% of total residential consumption is due to air conditioning. The National Energy
Secretary (SNE) project estimates that by 2050, 66% of homes will have at least one air
conditioning unit, which means that more than one million homes will have this equipment.
Knowing the total energy demand of the residential sector and the number of homes with
air conditioning, it is expected that, by 2050, 55% of residential consumption will be due to
this equipment, which is a significant increase.
On the other hand, Panama's ratifications of international agreements commit the country
to the goals of economic development and the environment, contributing to reducing
greenhouse gas emissions and making it possible to advance in the fight against climate
change. Therefore, the approval of building codes and standards is crucial for Panama.
Furthermore, being a country where more than 70% of electricity is consumed in houses and
buildings, it is required that architects, urban planners, and engineers take advantage of the
natural environment and prioritize bioclimatic design strategies with a view to the energy
efficiency of buildings (Secretaría Nacional de Energía, Plan Energético Nacional 2015-2050,
2016).
This research arises because a technical-scientific study is essential to guide the authorities,
urban planners, architects, and engineers of the Republic of Panama to energy development
conceived by implementing passive strategies, such as natural ventilation, in the
construction process of housing developments. Thus, the objective is to evaluate the
microclimate's effect on buildings’ thermal and energetic performance in an urban area of
the historical heritage in Panama City. This evaluation uses dynamic simulation, a
numerical approach, and existing indicators in humid tropical climates. The wind is the
most recommendable climatic parameter to take advantage of, so it is essential to study its
behavior to identify the architectural strategies and urban distribution to exploit suitable
natural ventilation.
2. Methodology
Building energy simulation software has gradually become widely used to evaluate
urban natural ventilation (Mirakhorli, 2017). Similarly, microclimate simulation tools that
offer the possibility of making small-scale climate predictions for different urban
configurations have been employed (DesignBuilder, n.d.). Therefore, integrating these
programs could be a solution that allows the quantitative evaluation of the effects of
microclimate on the thermal behavior of buildings and energy used in an urban context
(Mirakhorli, 2017).
For this study, an integrated simulation method was established to evaluate buildings'
natural ventilation potential and energy use in urban environments based on the urban
microclimate model ENVI-met and the building energy simulation software Designbuilder
(based on the EnergyPlus calculation engine).
Novasinergia 2022, 5(2), 132-157 136
ENVI-met is a prognostic, three-dimensional, non-hydrostatic, micro climatological model
based on computational fluid dynamics (CFD) and thermodynamics. It simulates
microclimate data, such as air temperature, relative humidity, wind speed, and mean
radiant temperature. Furthermore, it is a tool that calculates various processes such as
thermo-hygrometric exchange and bioclimatology. It can analyze the interactions between
architecture, atmosphere, and the surrounding urban environment. It estimates the energy
and the exchange processes between the exterior space and the interior environment of
buildings (Cordero, 2014).
DesignBuilder is a tool that identifies quickly and efficiently the design options that offer
the best performance in terms of cost, energy, and comfort. This module can be used to select
design solutions with greater energy, environmental and economic potential. Also, it can be
used to conduct a parametric analysis and sensitivity to identify the design variables with
the most significant and minor impact on the simulation results. This might be useful for
discarding unimportant design variables and focusing on those with the most significant
impact (DesignBuilder, n.d.).
The ENVI-met model is widely accepted and has several landscapes and built environment
studies (Morakinyo & Lam, 2016). This model was used to predict the outdoor thermal
environment concerning the urban configuration of the case study. The outdoor weather
boundary conditions adopted in that tool were derived from data observations at satellite
weather stations. In addition, a compendium of historical weather data from the CLIMdata
Solargis © service was used. Microclimate variables such as air temperature and relative
humidity were extracted from ENVI-met simulation results. Then, these variables were
used as input data to the DesignBuilder software. Microclimate effects on the building
energy performance could be incorporated into the DesignBuilder simulation to obtain the
indoor thermal conditions, as shown in figure 1. The DesignBuilder software was developed
in the United Kingdom; the version v7.0.0.116 was used in this study.
The qualitative method was used, which is related to the physical-geographical descriptions
of the study area, and the quantitative method, which refers to the environmental variables
(air temperature, relative humidity, wind speed, wind direction, and mean radiant
temperature). The case study consists of the urban area of Casco Antiguo in Panama City as
a reference to analyze the microclimate's effects and the indicators' performance that
allowed the evaluation of the natural ventilation potential through the established criteria.
This urbanization based on the standard construction style in Panama's context was
optimized for maximum energy use. It also analyzes the buildings' electricity consumption
using standard meteorological and microclimatic data. With the help of the DesignBuilder
software, the aim is to reduce unsatisfied hours of comfort and energy consumption.
Novasinergia 2022, 5(2), 132-157 137
Figure 1: General simulation methodology structure followed in this study.
2.1. Description of the case study
The urban area taken as a reference for this case study is the Casco Antiguo of
Panama City (Figure 2), which has spatial and environmental characteristics typical of its
socio-historic context. It was the second city of the Spanish colony in the Isthmus (1673). It
obeyed the urbanization plan legislated by the Laws of the Indies (1680), which established
guidelines on the design of the cities colonized by the Spanish Crown. Currently, the site is
considered a World Heritage Site by the United Nations Educational, Scientific, and
Cultural Organization (UNESCO), and it is maintained under Panama’s Historical Heritage
conservation regulations as a National Monumental Site (Mendes & Romero, 2021).
Figure 2: Casco Antiguo geographical area.
It has a checkerboard layout aligned to the four cardinal points where the main square
consists of the main church. As the city grew, the other surrounding squares emerged.
According to the vernacular constructions, the buildings maintain a norm of heights
between one and three naves, composed of calicanto, wood, clay blocks, and concrete. Most
of the roofs are of colonial tiles and a few slabs. The streets are made of red cobblestones;
they are 4.5 to 9 m wide (Mendes & Romero, 2021).
The Casco Antiguo study area is in the Awi climatic region according to the Köppen
classification (Atlas Nacional de Panamá, 1988): tropical climate (A) with the dry season (w)
Novasinergia 2022, 5(2), 132-157 138
during the northern hemisphere winter and slight thermal oscillation (i), also called tropical
savanna climate (Mendes & Romero, 2021). Therefore, the study area was classified as a
local climate zone type 3. This is because it presents a compact zone composed of low-rise
and high-density buildings. The study area presents buildings between one and three levels,
few trees, paved ground cover, and construction materials such as concrete and brick
(Cordero, 2014).
2.2. Microclimate assessment of the case study
An image of the Casco Antiguo was taken from Google Earth Pro © with Bitmap
extension (BMP); the image was cut to scale with the area's dimensions. This crop was 290
m (x) by 226 m (y), covering the Metropolitan Cathedral Church, the Independence Square,
and part of the Tomás Herrera Square.
We proceeded to define the pixel size of the space to be simulated (GRID): for (z), we used
twice the height of the highest building of the stage; for (x) and (y), we used values between
2 and 3. The pixel size should follow the stage so that it fits correctly to the details of the cut-
out surfaces.
The north is rotated so that the model is adjusted in an orthogonal format to the worktable's
axes (x, y), thus simplifying the modeling process. From this, we proceeded to construct the
model for the simulation of the urban microclimate. Finally, the information regarding
building heights, surface materials, pavements, and plant species compatible with the
software database was selected and placed (Figure 3).
Figure 3: Building heights and modeling of the Casco Antiguo cut-out.
The model was developed using the plugins included in ENVI-met software (ENVI-core,
BIO-met, and Leonardo). ENVI-core allowed the extraction of the maps that compile the
algorithms of the behavior of the modeled microclimate. BIO-met calculated the comfort
indices, and Leonardo generated the maps of the environmental variables (air temperature,
relative humidity, average radiant temperature, wind speed).
Only the most critical day of each month was used for the simulations due to limited access
to computing resources and time. These days were selected according to the highest air
temperature and highest direct solar radiation recorded. The meteorological conditions of
the most critical days of each month were obtained through a compendium of weighted
historical meteorological data from the CLIMdata Solargis © service. The data for the
simulation were extracted from this information (considered standard meteorological data),
Novasinergia 2022, 5(2), 132-157 139
as shown in Table 1. The wind direction was obtained using histograms where the most
frequent values were selected, and their average speed was obtained. For this case study,
2015 was chosen because it had the driest months and the lowest relative humidity (2015-
2019). According to the case study, a roughness factor value of 0.1 was used. The results for
the twelve months of the year were evaluated at the height of 1.5 m from the ground at 7:00,
15:00, and 21:00 hours. Each simulation lasted about 19 hours using a computer with 64 Gb
RAM, an Intel i7-9700F processor @ 3.0 GHz CPU speed, and eight cores.
Table 1: Weather conditions for each simulation.
Monthly
Critical Day
TMax (°C)
Hour
Tmin (°C)
Hour
HRMax (%)
Hour
HRMin (%)
Hour
Wind Speed
(m/s)
Wind
Direction
(°)
January 3
35.00
15:00
23.90
6:00
94.00
5:00
44.00
15:00
0.43
126.00
February 20
34.60
15:00
22.20
6:00
93.00
6:00
40.00
15:00
2.77
85.67
March 17
35.60
15:00
24.90
6:00
73.00
6:00
36.00
16:00
2.30
49.00
April 11
35.30
15:00
24.80
6:00
82.00
00:00
44.00
16:00
1.75
87.00
May 20
34.80
15:00
24.50
6:00
90.00
6:00
53.00
16:00
0.87
83.30
June 23
32.80
15:00
23.40
6:00
94.00
6:00
58.00
15:00
0.45
108.25
July 21
35.50
16:00
24.30
5:00
97.00
4:00
49.00
16:00
0.30
89.30
August 19
34.70
15:00
24.10
6:00
95.00
5:00
52.00
15:00
3.90
188.00
September 1
32.50
15:00
23.00
6:00
98.00
00:00
60.00
15:00
2.10
83.00
October 20
32.50
15:00
23.00
6:00
96.00
6:00
62.00
14:00
2.33
90.67
November 11
32.90
15:00
23.70
6:00
94.00
5:00
61.00
13:00
2.55
80.00
December 16
34.30
15:00
24.60
6:00
94.00
6:00
50.00
16:00
4.20
34.50
2.3. Microclimatic data generation from ENVI-met results
The outdoor air temperature and relative humidity are harmonic functions of time
with angular frequency ω and amplitude (Zhou, Zhang, Lin, & Li, 2007). Through
simulations in the ENVI-met software, these variables' monthly maximum, average, and
minimum values were obtained at 1.5 m. A time lag of 24 hours was considered where
. The 24 hours were recreated for all other days of the corresponding month. The
outdoor air temperature can be expressed as shown in equation (1), leading to equation (2)
(Zhou et al., 2007).
(1)
(2)
where
is the average outdoor air temperature, is the maximum outdoor air
temperature value recorded, t is the time in hours, and represents the phase shift.
Novasinergia 2022, 5(2), 132-157 140
The outdoor relative humidity was assumed to follow a behavior as expressed in equation
(3), which leads to equation (4).
(3)
(4)
where
corresponds to the average relative humidity and represents the recorded
maximum outdoor relative humidity value.
For the outdoor air temperature function, the offset was obtained by replacing the
maximum value at the corresponding time ( ) in equation (2), resulting in a
value of 3.93 rad. The same procedure was applied to obtain the offset in relative humidity
( ) with equation (4), resulting in 0.26 rad.
In addition, DesignBuilder also requires the dew point temperature (Tdp) corresponding to
the microclimate, which was calculated by employing both outdoor air temperature and
relative humidity in equation (5) (Lawrence, 2005):
(5)
In this way, the values of Tdp were obtained for the 24 hours of each day, using
the results of the ENVI-met simulation. Since the microclimatic data was performed for the
most critical day, the resulting 24-hour values for Tdp were adopted for the entire
corresponding month (i.e., every day in March has the same behavior as the most critical
day in the month). Other environmental variables, such as direct, diffuse solar radiation,
wind speed, and wind direction, are considered equal to those provided by the standard
meteorological data.
The microclimatic data resulting from the simulations in ENVI-met are presented in Figures
4-6 for each month throughout the year. The critical month corresponds to March, with the
highest outdoor air temperature recorded for the standard meteorological data (Table 1)
and the microclimatic data. The temperature levels in figure 4 show that external thermal
comfort is only achieved during the night and early morning hours. This is important as it
certainly benefits the nocturnal activities of the site; however, most of the cultural activities
take place during afternoon hours.
Figure 4: Monthly maximum, average, and minimum air temperature.
The values in figure 5 show that the month with the lowest relative humidity recorded was
March, considered the driest month. The relative humidity values higher than 100% are due
33.82
34.78 36.46 36.27 35.35
32.52 33.83 35.6
33.43 33.36 32.92 33.84
27.98
29.06 30.87 30.54 29.57
27.24 28.42 30.32 28.42 28.43 28.51 29.68
22.13
23.34 25.28 24.81 23.78 21.95 23.01
25.03 23.41 23.49 24.09 25.51
20
25
30
35
40
1 2 3 4 5 6 7 8 9 10 11 12
Air temperature (°C)
Months
Maximum Average Minimum
Novasinergia 2022, 5(2), 132-157 141
to the approximation used; however, they were limited to 100% in the simulations. The
highest humidity levels occur in the early morning hours. In contrast, the lowest values
occur in the midday or afternoon hours.
Figure 5: Monthly maximum, average, and minimum relative humidity.
In the case of the dew point temperature, as shown in figure 6, June has the highest value,
coinciding with the month with the highest relative humidity (Figure 6). However, the
graph also shows that a minimum value of 16.55 °C was reached just this month.
Figure 6: Monthly maximum, average, and minimum dew point temperature.
2.4. Urban settlement natural ventilation potential assessment
The NVCP was used as one of the indicators for assessing ventilation potential. This
indicator is based on air temperature and relative humidity (Yoon, Norford, Malkawi,
Samuelson, & Piette, 2020).. It is defined as the number of hours within a stipulated time
range. The local outdoor climatic conditions are favorable for using natural ventilation.
The criteria for lower and upper temperature were determined by the adaptive model
of the ANSI/ASHRAE 55 standard. Two thresholds were established to assess whether the
climatic conditions allow natural ventilation: the outdoor air temperature must be within
the comfort range established for indoor temperature variations, and the outdoor relative
humidity must be within the comfort range established for indoor relative humidity
variations as presented in equations (6) and (7) (Causone, 2015).
(6)
(7)
where and are the lower and upper indoor temperatures, respectively.
and are the lower and upper indoor relative humidity, respectively.
101.22
88.36
72.42 74.62
96.34104.73104.09
88.66 87.63 95.87 91.1989.74
75.91
65.11
55.56 59.66
75.69 82.38 82.05
69.62 72.67 78.06 76.7 73.02
50.59
41.86 38.69 44.7
55.04 60.03 56.71 50.57 57.7 60.24 62.23 56.3
35
55
75
95
115
1 2 3 4 5 6 7 8 9 10 11 12
Relative Humidity (%)
Months
Maximum Average Minimum
28.61
27.33
26.82
27.43
29.8630.89 29.8 28.9627.2928.4527.7228.09
23.16
22.0821.9822.47
24.705
23.7224.8324.24
22.955
24.0423.85
24.285
17.71
16.83 17.14 17.51
19.55
16.55
19.86 19.52 18.62 19.63 19.98 20.48
15
20
25
30
35
1 2 3 4 5 6 7 8 9 10 11 12
Dew Point Temperature
(°C)
Months
Maximum Average
Novasinergia 2022, 5(2), 132-157 142
The upper limit for indoor temperature ( ) was determined by applying the
ANSI/ASHRAE 55 (ASHRAE, 2013) standard procedure based on an acceptable 80%
environment (Causone, 2015), following equations (8) and (9).
(8)
(9)
where is the optimum comfort temperature, and is the monthly average outdoor
air temperature. For the lower limit () the following equation (10).
(10)
The lower and upper relative humidity thresholds were established, considering RH values
within the comfort range, generally between 30% and 70%.
Thus, the PCVN is calculated by adding the hours in which the climatic conditions meet
both criteria simultaneously, divided by the total number of hours in the year (Equation
(11)) (Causone, 2015).
(11)
where is equal to one, if the climatic condition at the hour of the stipulated time
range meets the established criteria and equals to zero otherwise; is the total number of
hours of the stipulated time in which the evaluation was performed.
2.5. Indoor thermal comfort and energy consumption assessment
The building block tool was used to construct the cut-out buildings in the
DesignBuilder software. The data on the dimensions of streets, buildings, squares, and
vegetation were obtained using Google Earth Pro.
The building block tool (light purple sections in figure 7) was used for uninhabited
buildings, churches, and historical ruins. As the model was being built, the information
regarding the heights and buildings’ levels were selected and placed using the Google Earth
cut-out as a guide. Then, in the DesignBuilder database, materials were selected according
to the actual characteristics of the surfaces, pavements, and plant species.
Figure 7: 3D model of the urban area under study in DesignBuilder.
The cut-out of the Casco Antiguo has a different occupancy profile depending on the
building typology. Around 50% of the buildings are establishments dedicated to
commercial activities, such as restaurants and bars, causing high flows of people on
weekends at night. Around 40% are facilities dedicated to hotel and residential services, and
around 10% are public offices and institutions (Padilla, 2016).
Novasinergia 2022, 5(2), 132-157 143
The town of San Felipe, where the Casco Antiguo is located, has a population of
approximately 3262 people (Padilla, 2016). The population density in the cut-out under
study is presented in figure 8; it was considered equivalent to the occupancy profile for the
simulation in the DesignBuilder software.
Figure 8: Population density in the case study.
Among the high-consumption equipment critical for characterizing the energy use profile,
the following were considered: air conditioning units, refrigerators (running 24 hours a
day), and fans (used during midday and afternoon hours). Multiple fluorescent lights were
also considered using an energy use profile at night and every morning (Table 2). Other
devices such as blenders, microwaves, television, and coffee maker were used in hotels,
restaurants, and residences. However, their use is minimal, so it was decided to omit them
within the characterization.
For the evaluation of thermal comfort, the operative temperature is employed as an indoor
temperature. According to Panama building regulations, indoor temperature levels must be
within 23.5 - 28.5 °C, with relative humidity between 60 - 80%.
Table 2: Equipment usage and occupancy profile.
Schedule
Occupation profile
0 - 0.005 pl/m2
0.0051 - 0.01 pl/m2
0.0101 - 0.05 pl/m2
0.0501 - 0.1 pl/m2
Mon to Fri: 8:00 to 18:00
Sat to Sun: 13:00 to 17:00
Luminaires (24 W)
Mon to Fri: 19:00 to 22:00
Sat to Sun: 19:00 to 5h00
Fans (70 W)
Mon to Fri: 12:00 to 16:00
Sat to Sun: 11:00 to 16:00
Computers (65 W)
Mon to Fri: 9:00 to 17:00
Refrigerator (145 W)
Sun to Sat: 0:00 to 23:59
Air conditioning unit
Mon to Fri: 9:00 to 17:00
Sat a Sun: 10:00 a 22:00
2.5.1. Passive cooling potential for indoor thermal comfort assessment
The Natural Ventilation Satisfied Hours (NVSH) is another indicator used to quantify
this potential. It is defined as the number of hours, within a stipulated time range, that the
indoor operating temperature, under natural ventilation conditions, can meet the thermal
comfort requirements. This indicator's definition considers the outdoor climate and the
buildings’ characteristics, such as geometry and thermal performance. The NVSH measures
Novasinergia 2022, 5(2), 132-157 144
the maximum number of hours when the outdoor climate is favorable for natural ventilation
inside buildings, based on the thermal comfort requirements established in the ASHRAE 55
standard (ASHRAE, 2013) regarding naturally conditioned areas (Tan & Deng, 2017).
The criteria for this indicator are based on the standard’s comfort range; this is defined as a
function of the average outdoor air temperature () and the operative temperature (). The
NVSH is calculated as the intersection of y for an acceptable 80% environment,
equations (12) and (13).
(12)
(13)
where is equal to one if the thermal comfort condition at the hour of the stipulated
time range meets the established criteria and is equal to zero otherwise; is the total
number of hours of the stipulated time in which the evaluation was carried out.
2.5.2. Energy performance for active cooling assessment
Electricity Consumption Indicator is used to quantify the active mode power
consumption of buildings; it is defined by equation (14).
(14)
where is the electricity consumption for air conditioning (air conditioning + mechanical
ventilation) for each building.
3. Assessment of passive strategies to improve building performance
The optimization method may require the application of various techniques so that
it can be successfully adapted to a specific case study (Belegundu & Chandrupatla, 2019).
For this optimization process, the microclimatic data generated from ENVI-met was used
and was performed for March through a sensitivity analysis and a parametric study using
the DesignBuilder software. The non-comfort hours are based on the ASHRAE 55
(ASHRAE, 2013) adaptive model of 80% acceptability. This software runs a series of
simulations and performs a screening process to identify the most important design
variables. These variables are combined until optimal and best-performing solutions are
achieved to discard unimportant options and focus on those with the most significant
impact (DesignBuilder, n.d.). The optimization was performed from two perspectives and
for two design criteria: passive and active modes.
3.1. Passive mode
Under the passive design criterion, the optimization process involves sensitivity
analysis that includes input variables such as the operating hours of external windows
(natural ventilation), window glazing, external wall construction material, and roof
construction material (Table 3). These parameters influence the non-comfort hours based on
the ASHRAE 55 adaptive model with 80% acceptability. A parametric study was also
Novasinergia 2022, 5(2), 132-157 145
performed to identify the best option out of certain combinations representing the optimal
solution and the best performance.
Table 3: Design variables options for the optimization process.
Window operation schedule
(natural ventilation)
Roof types
Wall construction
materials
Window glazing types
Off 24/7
Clay tiles (25 mm) on air gap
(20 mm) on roofing felt (5mm)
Lightweight superinsulated
Double Low-E Glass (e2=1) Clr
6mm / 6mm air
RestPub-EatDrink- Occ
(Mon to Fri: 8:00 to 18:00
Sat to Sun: 13:00 to 17:00)
Flat roof superinsulated
Project wall
Sgl Bronce 6mm
On 24/7
Flat roof U-value = 0.25 W/mk
Super insulated brick/ block external
wall
Sgl Clr 6 mm
Night ventilation schedule
19:00-07:00
Pitched roof – State of the art -
Heavyweight
Timber frame wall domestic (insulated
to 1995 regs)
Sgl Gris 6 mm
Pitched roof – Uninsulated -
Heavyweight
Uninsulated brick/ block wall
Sgl LoE (e2=2) Clr 3mm
Pitched roof – Uninsulated -
Lightweight
Wall – energy code standard - medium
weight
Dbl Bronce 6 mm/ 6mm air
Pitched roof – Uninsulated –
Medium weight
Wall – state of the art
Medium weight
Project pitched roof
Wall – uninsulated – medium weight
Sensitivity analysis identifies how the uncertainty in an output variable relates to the input
parameters, indicating which input variables most influence the simulation results. Four
input parameters were selected: external window operating hours, outer wall construction
material, pitched roof construction material, and window glazing type. The ASHRAE 55
adaptive comfort model with 80% acceptability was selected as the key performance
indicator for the output variable. Regression and random sampling methods were used for
this sensitivity analysis. The standardized regression coefficient (SCR), on the other hand,
generates the sensitivity of each input variable, thus identifying the most and least
important variables. In addition, other linear regression data such as the “adjusted R-
squared” and the “p-value” help determine the level of confidence and reliability of the
results (DesignBuilder, n.d.).
3.2. Active mode
The parametric analysis was performed for March under an active design criterion,
i.e., air conditioning. The most efficient options for electrical energy consumption for air
conditioning were identified through parameters such as roof type, insulation level, and
thermal mass of walls.
4. Results analysis
4.1. Microclimate natural ventilation potential assessment
The highest air temperature values are recorded at 15:00. In March, the maximum
temperature of 36.46°C was recorded (Figure 9a). These high temperatures in the afternoon
are due to the solar radiation that causes the heating of the surfaces and the air near them,
depending on the material that constitutes it (Guía de construcción sostenible para el ahorro
de energía en edificaciones, 2016). The albedo of the materials, i.e., the fraction of reflected
Novasinergia 2022, 5(2), 132-157 146
radiation concerning incident radiation, is another characteristic responsible for this energy
exchange process. Most of the materials used in the buildings of the Casco Antiguo are
opaque, and surfaces with high emissivity (e.g., cobblestones, concrete, tiles) predominate.
At 15:00, there is also a noticeable drop in relative humidity. This is due to
impermeable surfaces, solar radiation, and high air temperature. A minimum of 38.69% is
recorded in March, considered the driest month (Figure 9b).
On the other hand, the layout of the streets causes predominant winds parallel to the
streets. As a result, it presents a channeling effect, as shown in figure 9c. This channeling
effect in the streets causes prevailing velocities in the road co-rounds up to a maximum of
2.63 m/s for this specific case. In addition, the streets perpendicular to the wind experienced
the barrier effect on the windward side and the mat effect on the leeward side, registering
minimum flows.
The maximum mean radiant temperature value of 74.70°C corresponds to March; see
figure 9d. It is important to note that even the mustard-colored shaded areas maintain a
high mean radiant temperature. With the arboreal vegetation, there was a slight decrease in
the mean radiant temperature under the trees; a value of about 1 °C was observed, attributed
to the site's extensive impervious cover.
(a)
(b)
(c)
(d)
Figure 9: Microclimate results: (a) Air temperature, (b) Relative humidity, (c) Wind speed, and (d) Mean radiant
temperature.
The air temperature and relative humidity obtained through simulation in the ENVI-met
software establish how the outdoor climate presents favorable conditions for natural
ventilation. For calculating the NVCP, temperature and relative humidity limits were taken,
as shown in section 4.2, resulting in an annual range of 22.74 °C (June) - 30.87 °C (March)
and 30% - 70% relative humidity. In table 4, the comfort temperature calculated for each
Novasinergia 2022, 5(2), 132-157 147
month, the upper and lower temperature thresholds, and the hours of natural ventilation
climate potential NVCP indicator (%) are presented.
Table 4: Monthly upper and lower temperature ranges and natural ventilation climatic potential hours.
Months
Optimal Comfort
Temperature,
Tcomf
Lower Indoor
Temperature,
Tind, lower
Upper indoor
temperature,
Tind, upper
Hours
NVCP (%)
January
26.47
22.97
29.97
155
20.83
February
26.81
23.31
30.31
168
25.00
March
27.37
23.87
30.87
248
33.33
April
26.96
23.46
30.46
180
25.00
May
26.97
23.47
30.47
93
12.50
June
26.24
22.74
29.74
90
12.50
July
26.61
23.11
30.11
93
12.50
August
26.90
23.40
30.40
155
20.83
September
26.61
23.11
30.11
150
20.83
October
26.61
23.11
30.11
124
16.67
November
26.64
23.14
30.14
120
16.67
December
27.00
23.50
30.50
124
16.67
Annual
-
-
-
1700
19.41
The NVCP results show how the microclimate generated in a built space can significantly
affect the natural ventilation potential. The higher the NVCP value, the higher the climatic
potential for natural ventilation.
The highest value of NVCP occurred in March because the indicator instructions point to
air temperature and relative humidity as input parameters. In other words, although March
has the year’s highest temperatures, it shows more hours of ventilation potential because it
is the month with the lowest relative humidity.
The annual NVCP indicates that 19.41% of the year, there are adequate microclimatic
conditions that allow the use of natural ventilation. This is a low percentage since the
recommended percentage, according to Causone (2015) is 47% when natural ventilation can
ideally be used 47% of the time during the year.
It is important to note that Panama has a warm, humid climate. Therefore, microclimatic
parameters in an urban sector (such as unfavorable wind directions, high temperature, and
relative humidity), urban morphological factors (such as high building density, unfavorable
H/W ratio, urban obstructions, inadequate orientation and geometry of buildings), scarcity
of vegetation, and the general urban design of the site, can affect the number of hours in
which natural ventilation can be used as a passive cooling strategy.
4.2. Comfort and energy performance assessment
A quantitative index representing the correlation between NVSH and NVCP is
shown in table 5. These results implied that, although the potential of natural ventilation
was significantly influenced by the external microclimate (NVCP), the effects of internal
building characteristics, such as design, on the internal heat gains and envelope thermal
performance are factors that must be considered and cannot be underestimated when
natural ventilation is to be used as a passive technique.
Novasinergia 2022, 5(2), 132-157 148
Table 5: Natural ventilation satisfied hours (NSVH).
Months
Hours
NVSH (%)
h*/h** (%)
February
28
4.17
16.67
October
124
16.67
100
November
120
16.67
100
Annual
272
3.11
16
* Hours from NVSH indicator for the three months. ** Hours from PCVN indicator for the three months.
The NVSH confirms the thermal unconformity recorded inside the buildings according to
the operating temperature resulting from the dynamic simulations performed in passive
mode. However, the evaluation of the NVSH indicator could only be performed for three
months because they were within the allowable operating temperature range, yielding
percentages that are considered very low (Causone, 2015).
Table 6 presents the monthly critical day consumption using standard meteorological and
microclimatic data. The annual consumption recorded using the microclimatic data is
higher (149646 kWh) than that of the standard meteorological data (143536 kWh).
Table 6: Energy consumption indicator (ECI).
Months
Monthly critical day
consumption
Standard weather data (kWh)
Monthly critical day
consumption
Microclimatic weather data
(kWh)
Percentage
Difference, (%)
January
13382
13503
0.90
February
12577
13547
7.72
March
9597
10537
9.79
April
13256
13578
2.43
May
14051
14438
2.75
June
9628
10106
4.96
July
11756
11272
4.12
August
13384
13992
4.54
September
9961
10280
3.20
October
9793
10457
6.78
November
12934
14032
8.49
December
13217
13904
5.20
Annual
143536
149646
4.26
Figure 10 compares the resulting electricity consumption using standard meteorological
data (light blue) and microclimatic data (dark blue). The highest electricity consumption
coincides with the month whose operating temperature resulted in the highest values.
Figure 10: Electricity consumption using standard weather data.
Novasinergia 2022, 5(2), 132-157 149
The results show a significant difference for each month, where the highest percentage
difference is 9.79% (940 kWh), corresponding to March. The lowest is 0.90% (121 kWh),
corresponding to January, which is important in energy consumption and costs. This
indicates that the differences are due to the use of air conditioning. However, the difference
between electricity consumption does not seem to follow a clear trend since a considerable
variation by month is found, even if the internal heat gains due to occupancy, lighting, and
equipment were kept the same for each case (under standard microclimatic and
meteorological data).
In addition, given that the resulting electricity consumption is higher when using
microclimatic data, with an average percentage difference of 5.07%, this sets an important
precedent for future energy simulation in this case study, when high accuracy is required.
Finally, it is important to highlight that the type of urban area studied, in this case, is not
typical of the other urban spaces in Panama City. Therefore, it may be necessary to perform
a similar study for other areas.
4.3. Assessment of passive strategies to improve building performance
In table 7, the standardized regression coefficient (SRC) indicates the relative
sensitivity of the input variables to the output. Its absolute value ranks them in order of
sensitivity and importance. The sign represents whether the relationship with the output is
direct or inverse. For example, the glazing type strongly influences the adaptive comfort
model’s output variable. It is directly related since increased glazing leads to an increase in
the adaptive comfort model. This can be explained by the fact that an increase in glazing
decreases the solar gains through the windows. In addition, the adaptive comfort model is
strongly influenced by the operating hours of the external windows; however, it presents
an inverse relationship. Furthermore, it was moderately influenced by the construction
material of the roof and external walls.
Table 7: Sensitivity analysis results in passive mode for Adaptive Comfort ASHRAE 55 – 80% acceptability.
R2=0.10
Standardized regression
coefficient, (SRC)
“p-value”
Glazing type
0.28
0.00
External window operation schedule
-0.22
0.03
Pitched roof construction
-0.05
0.60
External wall construction
0.05
0.61
The “p-value” in the green color represents high importance, while those in red are
irrelevant. This indicates that, in passive mode, the most significant parameters are window
glazing and window operating hours.
The value of “R2” is 0.10, considered low, indicating that the actual input variables cannot
usefully explain the uncertainty in the output. However, the relationship of each variable to
the output variable may be relevant. On the other hand, a “p-value” higher than 0.05
indicates that there is a low level of confidence in their results, such as the case for the
variables of pitched roof construction (0.60) and external wall construction (0.61).
On the other hand, it was found that the recorded minimum range of hours without comfort
was 488.8 - 489.7. Thus, the month of March has 527 hours of total occupancy, which means
that, in passive mode, about 93% of the time, there are no comfortable conditions inside the
buildings.
Novasinergia 2022, 5(2), 132-157 150
4.3.1. Passive mode
The analysis shows that roofs with different insulation and thermal mass levels and
window operation schedules do not generate significant changes in the hours of non-
comfort (Figure 11). The average value recorded was 495 hours; taking as a reference the
527 hours of total occupancy in March results that 94% of the occupancy time remains in
non-comfort conditions. Also, it can be observed the non-comfort hours when applying the
different types of glazing for the four window operation schedules. Therefore, the optimal
combination is the Bronze 6 mm material with a 24/7 window operation schedule.
Figure 11: Discomfort hours resulting from using different window glazing, wall construction, and roof types in passive
mode.
4.3.2. Active mode
Moreover, another parametric analysis was performed in active mode. The results
showed that the combination of uninsulated medium-weight wall and clay tiles roof
(current materials) has one of the highest average electricity consumptions for March out of
the eight defined combinations.
Novasinergia 2022, 5(2), 132-157 151
However, the superinsulated brick/block external wall and flat roof superinsulated
combination present the lowest electrical consumption. Therefore, according to the
performance and design objective, this is a good combination of variables.
The variation of materials concerning power consumption is significant. It is shown that the
material of the walls and roof is a critical parameter and that replacing it with a
superinsulated and low thermal mass material has a significant effect on the electrical
consumption for air conditioning, resulting in a percentage difference of 12.6%, which
translates into a decrease of 38485.10 kWh in consumption for air conditioning (Figure 12).
Figure 12: Energy consumption by air conditioning resulting from current materials and proposed materials by parametric
study.
5. Discussion
The results can be analyzed from two perspectives: passive and active modes. The
microclimate and its natural ventilation potential are evaluated in the passive mode through
the NVCP indicator. The indoor thermal comfort of the buildings is analyzed through the
NVSH indicator. On the other hand, in the active mode, the electricity consumption for air
conditioning is compared through the ECI indicator using standard meteorological and
microclimatic data. With both techniques (passive mode and active mode), optimization
methods were applied based on sensitivity analysis and parametric studies considering
input variables (envelope materials, glazing, and window operating hours) and the output
variable (hours of non-comfort according to the ASHRAE 55 adaptive model - 80%
acceptability).
The result of the NVCP (Table 4) indicates that 19.41% of the year, under air temperature
and relative humidity conditions, an outdoor climate (microclimate) favorable for natural
ventilation is present. However, according to the one recommended (Causone, 2015), it is a
meager percentage. Similarly, this percentage can be justified to some extent by the annual
thermal behavior of the climate (Padilla, 2016), which indicates that 71% of the annual hours
in Casco Antiguo would require cooling. It is important to mention that Panama has a
tropical climate with high temperatures and humidity. Therefore, this could be one of the
main reasons that affect the hours in which natural ventilation (according to the indicator's
input parameters) is technically favorable as a passive cooling strategy.
The satisfying hours of natural ventilation (NVSH) could only be evaluated for February,
October, and November (Table 5), because they were the months within the allowable
operating temperature range, according to the ANSI/ASHRAE 55 standard as the thermal
268111.80
306596.90
240000 260000 280000 300000
Super insulated brick/block
external wall + Flat roof
superinsulated
Wall uninsulated medium
weight + Clay tiles
Energy consumption (kWh)
Novasinergia 2022, 5(2), 132-157 152
comfort range for naturally conditioned areas. This indicates that the rest of the months
presented non-comfort conditions during the occupancy period of the buildings. Thus, the
annual NVSH was 3.11%, thus confirming the thermal discomfort from the outdoor and
indoor operating temperature results from the dynamic simulations. The microclimate
around the buildings shows that natural ventilation does not favor indoor thermal comfort,
although the wind enters the buildings. Comparing electricity consumption results when
implementing the standard meteorological data and the microclimatic data, it can be
observed that higher consumption values are recorded using the microclimatic data. This
can be justified because the climatic conditions of the built space are affected by
environmental variables (relative humidity, wind speed and direction, air temperature,
solar radiation) and urban morphological factors (building density, construction materials,
orientation and geometry of buildings, H/W ratio, vegetation) that affect the indoor thermal
comfort of buildings, which leads users to opt for air conditioning systems, thus increasing
electricity consumption. The results (Figure 10) show that the most significant percentage
difference was 9.79%, equivalent to 940 kWh corresponding to March, and the lowest was
0.90%, equivalent to 121 kWh corresponding to January. These values are essential in terms
of energy consumption and costs.
Regarding the optimization process, the passive mode uncertainty analysis indicates that
the minimum hours out of comfort conditions, based on the ASHRAE 55 adaptive model
(80% acceptability), are 488.8; this is a very high value when compared to the 527 hours of
total occupancy for March. Furthermore, the sensitivity analysis (Table 7) indicates that
glazing and window operating hours are the variables that most influence the hours without
comfort. At the same time, roof materials and external walls are parameters that do not
generate significant changes.
The parametric study in passive mode (Figure 11) shows that roofs with different insulation
and thermal mass record values vary between 491 and 495 hours. The same occurs when
applying different construction materials to the external walls. As a result, the hours outside
the comfort conditions vary between 492 and 496. Although the glazing type is one of the
variables that reduce the number of thermal discomfort hours compared to that of the others
(roof materials and external walls), its minimum value recorded was 488.8, indicating that
it is still a parameter that does not reflect significant changes since the reduction is only 6%.
This means that 92.75% of the occupancy time remains outside the comfort conditions under
a passive design criterion.
On the other hand, the parametric study in active mode reflects essential changes in
envelope materials concerning electricity consumption. For example, combining a
superinsulated exterior wall and a superinsulated roof generates a difference of 12.6%,
equivalent to decreasing 38485.10 kWh in electricity consumption for air conditioning.
Although materials recommended by bioclimatic (uninsulated envelopes and low thermal
mass) were considered within the design options, they did not generate improvements
concerning the hours of thermal discomfort in passive mode. The literature indicates that
such recommendations are sufficient for individual and remote buildings; however, there
were no favorable results when applied in an urban area, such as this case study. This could
be attributed to the proximity between buildings since the urban construction design of the
Novasinergia 2022, 5(2), 132-157 153
Casco Antiguo consists of minimal or almost no distance between buildings, forming
compact building blocks.
This study is the starting point for future experimental research by evaluating the natural
resource potential and its energy use using dynamic simulation. It can be applied to
implementing this passive technique in urban scale buildings in the country.
6. Conclusions
The natural ventilation potential of the Casco Antiguo urban area was evaluated
using the NVCP indicator. A methodology based on dynamic energy simulations using
ENVI-met and DesignBuilder software was used to evaluate the indoor thermal comfort of
the buildings. The adaptive comfort model ASHRAE 55 with 80% acceptability was used
through the NVSH indicator to evaluate the satisfied hours of natural ventilation. The main
findings of the study can be summarized as follows:
The potential to use natural ventilation to maintain indoor thermal comfort is significantly
altered by the microclimate of the built space and outdoor conditions. The NVCP result
indicates that 19.41% of the year, under air temperature and relative humidity conditions, a
favorable climate for natural ventilation is considered low.
The indoor operating temperature was higher than the upper thermal comfort threshold
(set by the NVSH indicator) during most periods in passive mode. The annual NVSH was
3.11%, which indicates that most months present non-comfort conditions during the
building occupancy and thus confirms the thermal discomfort evidenced from the results
of the dynamic simulations. This indicates that the indoor thermal comfort requirements
could only be met by active air conditioning techniques (air conditioning + mechanical
ventilation) throughout the year.
When implementing the standard meteorological and microclimatic data, the electricity
consumption results showed a significant difference of 9.79%, equivalent to 940 kWh
corresponding to March, and the lowest of 0.90%, equivalent to 121 kWh corresponding to
January. These values are important in terms of energy consumption and costs.
The optimization process showed that the envelope materials do not generate significant
changes concerning the hours of thermal discomfort in passive mode. However, energy use
in active mode was reflected by implementing superinsulated walls and ceilings; a
difference of 12.6%, equivalent to decreasing 38485.10 kWh in electricity consumption for
air conditioning, was the result of the simulation. It was possible to demonstrate an energy
use due to the characteristics of the envelopes but not due to the implementation of natural
ventilation as a passive air conditioning technique.
The results show that natural ventilation is insufficient to guarantee thermal comfort inside
the buildings for this case study. The high microclimate temperature causes higher outdoor
heat gains. The spatial distribution of air temperature is related to wind direction and solar
radiation. The building design forming compact building blocks and leaving no space
between them should be a focal point. A study based on the various spatial distribution of
a set of buildings is required to analyze the relationship between the location of the
Novasinergia 2022, 5(2), 132-157 154
buildings and the wind in an outdoor environment. By doing so, the formation of ventilation
corridors that favor airflow would be evaluated, thus preventing the structures from
causing the barrier effect, as evidenced in the results. Proper hyperdynamic layout and
variation of building heights could allow for wind insertion into facades and buildings.
Applying these modifications to the case study would be difficult because it is an area
considered a historical heritage of humanity and is protected by UNESCO. However, these
recommendations can be taken as a reference and applied in the design stage of new
projects.
Methods to mitigate thermal stresses caused by solar radiation should be considered,
including urban planning and alternative construction materials for buildings and
pavements. Tree vegetation and bodies of water in urban areas can positively impact the
outdoor microclimate. In addition, the planning, length, spacing, and shape of buildings
should be reasonably allocated.
This study considered only the urban area of Casco Antiguo, being a different area from the
rest of Panama City due to its construction design. Therefore, it is necessary to conduct
further research to analyze the variation of natural ventilation potential in the rest of the
country. Likewise, since this study is based on dynamic simulations through computational
tools and methods used in the literature, further validation studies based on experimental
tests with outdoor and indoor field measurements are required to compare the results.
The findings and the simulation approach presented are expected to help urban planners,
architects, and engineers in Panama quantify the potential for using natural ventilation in
different country areas. Besides, it is expected to be used as a reference guide to make
observations on the climate for executing architectural and urban design projects to allow
optimal natural ventilation in Panama buildings.
Authors’ contributions
Following the internationally established taxonomy for assigning credits to authors
of scientific articles (https://casrai.org/credit/). Authors declare their contributions in the
following matrix:
Rodriguez, K.
Mora, D.
Chen. M.
Conceptualization
Formal analysis
Investigation
Methodology
Resources
Validation
Writing – revision and editing
Novasinergia 2022, 5(2), 132-157 155
Conflicts of Interest
The funders had no role in the study's design in collecting, analyzing, or interpreting
data in the writing of the manuscript or in the decision to publish the results.
Acknowledgment
The authors would like to thank the Technological University of Panama and the
Faculty of Mechanical Engineering for their collaboration, together with the Research Group
Energética y Confort en Edificaciones Bioclimáticas (ECEB) https://eceb.utp.ac.pa/. This
research was funded by the Panamanian Institution Secretaría Nacional de Ciencia,
Tecnología e Innovación (SENACYT) https://www.senacyt.gob.pa/ , under the project code
FID18-056, as well as supported by the Sistema Nacional de Investigación (SNI).
References
ASHRAE. (2013). Standard 55: Thermal environmental conditions for human occupancy.
Retrieved from: https://www.ashrae.org/technical-resources/bookstore/standard-55-
thermal-environmental-conditions-for-human-occupancy
Atlas Nacional de Panamá (1988). Mapas de Climas de la República de Panamá, Retrieved from:
https://www.inec.gob.pa/Archivos/P28813.pdf
Belegundu, A. D., & Chandrupatla, T. R. (2019). Optimization Concepts and Applications in
Engineering, (3rd ed.). Cambridge: Cambridge University Press.
https://doi.org/10.1017/9781108347976
Bouchahm, Y., Bourbia, F., & Bouketta, S. (2012). Numerical Simulation of Effect of Urban
Geometry Layouts of Wind and Natural Ventilation Under Mediterranean Climate.
In Conference 6th ASCAAD, 2012. Retrieved from:
https://www.researchgate.net/publication/322743803_Numerical_Simulation_of_Eff
ect_of_Urban_Geometry_Layouts_of_Wind_and_Natural_Ventilation_Under_Medi
terranean_Climate
Causone, F. (2015). Climatic potential for natural ventilation. Architectural Science Review,
59(3), 212-228. http://dx.doi.org/10.1080/00038628.2015.1043722
Cordero, X., (2014). Microclima y Confort Térmico Urbano: Análisis sobre la influencia del
amorfología del cañon .Urbano, caso de estudio en los barrios del Raval y Gracia,
Barcelona (Trabajo final de Máster en Arquitectura, Energía y Medio Ambiente).
Escuela Técnica Superior de Arquitectura de Barcelona. Barcelona: Españan.
Retrieved from:
https://upcommons.upc.edu/bitstream/handle/2099.1/23637/XimenaCordero_TFM.p
df
DesignBuilder. (n. d.). Módulo Optimización. Retrieved from:
https://designbuilder.co.uk/optimisation
Guía de construcción sostenible para el ahorro de energía en edificaciones (2016). Retrieved
from:
https://www.gacetaoficial.gob.pa/pdfTemp/28165/GacetaNo_28165_20161124.pdf
Novasinergia 2022, 5(2), 132-157 156
He, J., Ding, L., & Prasad, D. (2020). Wind-sensitive urban planning and design: Precinct
ventilation performance and its potential for local warming mitigation in an open
midrise gridiron precinct. Journal of Building Engineering, 29. 101145.
https://doi.org/10.1016/j.jobe.2019.101145
Jiang, Y., Wu, C., & Teng, M. (2020). Impact of Residential Building Layouts on Microclimate
in a High Temperature and High Humidity Region. Sustainability, 12(3).
https://doi.org/10.3390/su12031046
Kastillo, J. P., & Beltrán, R. (2015). Optimización energética para el aprovechamiento de ventilación
natural en edificaciones en climas cálidos de Ecuador (Proyecto previo a la obtención del
título de Ingeniero Mecánico). Escuela Politécnica Nacional, Quito:Ecuador.
http://dx.doi.org/10.13140/2.1.1704.4803
Kusumastuty, K. D., Poerbo, H. W., & Koerniawan, M. D. (2018). Climate-sensitive urban
design through Envi-Met simulation: A case study in Kemayoran, Jakarta. IOP
Conference Series: Earth and Environmental Science. 129. 012036.
http://dx.doi.org/10.1088/1755-1315/129/1/012036Lawrence, M. (2005). The
Relationship between Relative Humidity and the Dewpoint Temperature in Moist
Air. A Simple Conversion and Applications. Atmospheric Bulletin of the American
Meteorological Society, 86(2), 225-234. https://doi.org/10.1175/BAMS-86-2-225
Lin, S. H. (2014). Designing in performance: Energy simulation feedback for early-stage design
decision making (PhD thesis). University of Southern California, CA-USA. Retrieved
from: https://www.researchgate.net/publication/260714182_Designing-
in_performance_energy_simulation_feedback_for_early_stage_design_decision_ma
king
Meiss, A., Padilla, M., & Feijo, J. (2017). Methodology Applied to the Evaluation of Natural
Ventilation in Residential Building: A Case Study. Energies, 10(4).
https://doi.org/10.3390/en10040456
Mendes, Á., & Romero, M. (2021). Microclimas urbanos en la ciudad de Panamá: Estudio de
tres recortes históricos de la ocupación urbana. Paranoá 30.
https://doi.org/10.18830/issn.1679-0944.n30.2021.03
Mirakhorli, A. (2017). Natural Ventilation in Residential Building: An EnergyPlus Simulation
Study. In Project: Residential Buildings Efficiency, the University of Texas at San
Antonio. TX:USA. Retrieved from:
https://www.researchgate.net/publication/319162483_Natural_Ventilation_in_Resid
ential_Buiding_An_EnergyPlus_simulation_study
Morakinyo, T., & Lam, Y. (2016). Simulation study on the impact of tree configuration,
planting pattern, and wind condition on street canyons' micro-climate and thermal
comfort. Building and Environment, 103. 262-275.
https://doi.org/10.1016/j.buildenv.2016.04.025
Padilla, C. (2016). Plan integral para la mejora de la movilidad y seguridad vial para el centro
histórico de la ciudad de Panamá (Reporte técnico E.1). Alcaldía de Panamá y Banco
Interamericano de Desarrollo. Retrieved from: https://dpu.mupa.gob.pa/wp-
Novasinergia 2022, 5(2), 132-157 157
content/uploads/2017/06/20175-E.3-002-
R01_INFORME_FINAL_ESTRATEGIAS_DE_MOVILIDAD_CH_PANAMA.pdf
Pérez, Y. (2018). Estrategias de ventilación natural en climas tropicales a partir del comportamiento
del viento sobre edificios ubicados en espacios urbanos mediante la simulación de programas
de diseños interactivos (Trabajo final de máster, Universidad Politécnica de Catalunya),
Catalunya: España. Retrieved from:
https://upcommons.upc.edu/handle/2117/122457
Priyadarsini, R., C. K., Hien, & Wai, C., (2008). Microclimatic modeling of the urban thermal
environment of Singapore to mitigate urban heat island. Solar Energy, 82(8), 727-745.
https://doi.org/10.1016/j.solener.2008.02.008
Secretaría Nacional de Energía, Plan Energético Nacional 2015-2050, (2016). Retrieved from:
https://www.senacyt.gob.pa/wp-content/uploads/2018/12/3.-Plan-Energetico-
Nacional-2015-2050-1.pdf
Tan, Z., & Deng, X. (2017). Assessment of Natural Ventilation Potential for Residential
Buildings across Different Climate Zones in Australia. Atmosphere, 8(9).
https://doi.org/10.3390/atmos8090177
Tong, Z., Chen, Y., & Malkawi, A. (2016). Defining the Influence Region in neighborhood-
scale CFD simulations for natural ventilation design. Applied Energy, 182, 625-633.
http://dx.doi.org/10.1016/j.apenergy.2016.08.098
Yi, C. Y., & Peng, C. (2014). Microclimate Change Outdoor and Indoor Coupled Simulation
for Passive Building Adaptation Design. Procedia Computer Science, 32, 691-698.
http://dx.doi.org/10.1016/j.procs.2014.05.478
Yoon, N., Norford, L., Malkawi, A., Samuelson, H., & Piette, M. A. (2020). Dynamic metrics
of natural ventilation cooling effectiveness for interactive modeling. Building and
Environment, 180. https://doi.org/10.1016/j.buildenv.2020.106994
Zhou, J., Zhang, G., Lin, Y., & Li, Y. (2007). Coupling of thermal mass and natural ventilation
in buildings. Energy and Buildings, 40(6). 979-986.
https://doi.org/10.1016/j.enbuild.2007.08.001
