This document discusses the design parameters for optimizing zero energy buildings (ZEBs) using the TARP optimization tool. It describes using EnergyPlus software to simulate a low utilization building at GIET University in India. The initial and optimized building parameters are compared to evaluate the annual energy consumption and reduce usage. Key factors like thermal comfort zones, energy demand, and electricity consumption are analyzed. The goal is to develop an optimized building design approach to help construct more efficient, low-cost ZEBs.

1. XXX-X-XXXX-XXXX-X/XX/$XX.00 ©20XX IEEE
.
DESIGN PARAMETERS FOR THE TECHNICAL OPTIMIZATION
OF ZEB USING TARP
B. Vikram Anand
PhD Scholar, Dept. of EEE
GIET University
Gunupur, Odisha
bvikram@giet.edu
Dr. Sanjay K Kuanar
Professor, Dept. of CSE
GIET University
Gunupur, Odisha
sanjay.kuanar@giet.edu
G R K D Satya Prasad
Professor, Dept. of EEE
GIET University
Gunupur, Odisha
grkdsp@giet.edu
Abstract—— This paper focuses on the aspect of building
design which inculcates a concept oriented systematic procedure
and computer-based simulation. Apparently, the main intent is
to utilize the optimization tools for performing tasks without
exchanging the criterions like energy efficiency, energy
demand, heating and cooling demands, total electricity
consumption and the impact of environmental conditions.
Through this study, we have made an approach to resolve the
fore-coming challenges in construction of an optimized
building. The method involves the combined use of Energy Plus
associated with TARP optimization tool to design the interior
and exterior of the building. For an instance, the anamnesis of
a low utilization building located in GIET UNIVERSITY,
Gunupur has been simulated and the results are validated
successfully. To evaluate the annual energy consumption of the
building, the comparison between initial and final parameters
are carried out with the help of Energy Plus and optimization
tools. Eventually, the optimal design emanated has extricated
the building parameters such as comfort zones, discomfort
zones, also the thermal analysis of various criterions of the
optimization helped in laying a successful path to Zero Energy
Building [ZEB].
Keywords: Zero energy building, design, optimization,
TARP, Comfort Zones, Energy Plus, temperature and day
lighting control.
I.INTRODUCTION
Saving energy has become a challenging issue in the
present scenario. Recently buildings are evolving into zero
energy buildings as they play major role in energy
consumption utilizing 40% from the fossil fuels worldwide
and are the major contributors of greenhouse gases. Zero
energy building which is capable to generate power by itself
through the renewable energy, majorly focuses on reducing
energy consumption, increase efficiency and optimize the
design model as well as cost. Therefore, we proposed an
intelligent energy saving system based on optimization of
building using various tools like Energy Plus, Open studio
and Google sketch up which lays a significant platform for
simulating various performance parameters like cost,
energy efficiency and use of renewable energy onsite. An
effective method to optimize the design of the rural building
for the purpose of reducing energy consumption in building
envelope has been proposed [1]. This paper proposes the
optimized methodology among various influential factors
affecting the performance parameters of a zero-energy
building.
System based Simulation approach is initially adopted
before undertaking the real subject, for performance
evaluation and to decrease the failure chances for desired
specifications, in addition to optimize system performance.
In this paper, Energy plus using tarp algorithm-based
simulation have been adopted to obtain the appropriate
indoor conditions, with lower costs resulting in energy
consumption.
Heat transfer of building structure is very complex,
based on following facts:
a) Complex domain of the building envelope, such as walls,
roofs, etc consists of a wide variety of materials with
different thermal properties.
b) Seasonally changing in outdoor conditions due to weather
variation like solar radiation, wind speed and temperature.
c) Constantly changing in indoor temperature due to
variations in indoor thermal loads.
Heat transfer in a building takes place because of
conduction, convection and radiation
1) Conduction
Conduction process refers to the heat transfer from the
high temperature surface to low temperature part and is
given by equation (1)
= − ………… (1)
Where:
Q [W]: heat transfer rate.
T [K]: temperature.
X [m]: distance.
K [W/make]: thermal conductivity of the material.
A [m2]: cross-sectional area of heat path.
Under the steady state conditions the temperature
distribution is linear, and the temperature gradient may be
expressed by the relation (2)
= ………………… (2)
Where:
L [m]: thickness of heat path.
T1 [K]: first temperature.
Figure (1): Model of considered building
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2. T2 [K]: second temperature.
In buildings, conduction occurs through solid walls that
are not in thermal equilibrium.
2) Convection
Convection heat transmit occur as the liquid flow tender a
humid body. Fluid next to the body constitutes are supposed
to be boundary layer, where the velocity of the fluid on the
exterior is equivalent to zero. It is expressed as
= ℎ ( − ) …………………….. (3)
Where:
Q [W]: heat transfer rate.
A [m2]: surface area.
Ts [K]: wall (surface) temperature.
T∞ [K]: fluid temperature.
H [W/m2.K]: convection heat transfer coefficient
3) Radiation: In buildings, conduction occurs through solid
walls that are not in thermal equilibrium and it is given as
= …………………... (4)
II. DESCRIPTION OF THE PROJECT BASED
SOFTWARE
This work presents Energy Plus 9.0.1, optimization
program specially designed to find the user selected
parameters to reduce objective function like annual power
usage, investment and exploitation cost. It allows
simulation data link with text based I/O by modifying the
configuration file without the requirement of modifications
in code. This software has an open interface with the
simulation program side and minimization algorithm side
which runs either on GUI or as a console application. It can
easily couple to the program like Open Studio [2]
III. DESIGN IMPLEMENTATION OF PROPOSED
SYSTEM
• Computational methodology applied to sustainable
zero energy building available in the GIET
University college campus where sequential
simulation approach is used for estimating insulation
of the building envelope, Thermal performance and
annual energy consumption. The optimization tool
used is Energy plus 9.0.1 which selects the optimal
parameters associated with the minimal energy
consumption. Practically, computational design
analysis is performed on virtual building to provide
easy option for a researcher to measure output. This
way of measuring parameters creates the accurate
analysis for result estimation. Various indoor and
outdoor parametric analysis under various climatic
condition is possible with the designed optimization
tool [3].
• Main Objectives of proposed system are:
• To evaluate the cost analysis and energy efficiency
parameters
• To differentiate between actual and optimized
building performance.
• The proposed system used reduced sequential
simulation approach to achieve the goal of zero
energy building
• Desired building is carried out based on virtual
experiment on a building model in college campus
which can compare the possible outcomes laying a
path towards net zero energy building
IV. METHODOLOGY
Modeling is to find mathematical equations for
achieving the law of conservation of mass and energy by
dynamic analysis of virtual system parameters. Some
methods are used for simulating the energy consumption
in buildings, so as to meet the different requirements.
energy flow and various heat transfer properties need to
be well established for indoor climate [4]. Floor plan heat
gain, thermal analysis, roof structure, ventilation
parameters, ceiling, internal heat of the body need to be
evaluated for better performance of the normal building
to zero energy building.
The basic model includes all the parameters to be
justified under initial conditions taken such as HVAC
measure, energy efficiency measures, indoor and outdoor
conditions based on interaction with seasonal variations
due to which temperature varies. Energy conservation
must be balanced with the chosen values for analysis.
And these changing variables with respect to time must
be considered [5].
Table 1: Building parameters for external wall to window
A. Dimensional measurement analysis:
TOTAL NORTH
(315 to 45
Deg)
EAST
(45 to 135
Deg)
SOUTH
(135 to 225
deg)
GROSS
WALL
AREA
[m2
]
101.56 25.97 24.81 25.97
ABOVE
GROUND
WALL
AREA
[m2
]
101.56 25.97 24.81 25.97
WINDOW
OPENING
AREA
[m2
]
4.13 2 1.01 0
GROSS
WINDOW
WALL
RATIO
[%]
4.06 7.7 4.08 0
ABOVE
GROUND
WINDOW
WALL
RATIO
[%]
4.06 7.7 4.08 0
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3. The common modeling approaches of detailed energy
simulation can be summarized as
• Response functions under frequency domain &
time domain.
• Numerical method using finite element approach
& finite differences
• Numerical methodology using control volume
heat balance
b. Model of heat transfer through external walls.
Heat transfer through external side including the suns total
radiations such as beam and diffuse [6].
For the above layer the energy balance equation is
designed and described as below.
.
( – ) −
. .
. .
…… (5)
Where:
ρ1 [kg/m3]: density of wood layer (first layer inside).
cp1 [J/chg.]: specific heat capacity of wood layer (first layer
inside).
V1 [m3]: volume of wood layer (first layer inside).
k2 [W/make]: thermal conductivity of first concrete layer (second
layer inside).
l2 [m]: thickness of first concrete layer (second layer inside).
A2 [m2]: area of first concrete layer (second layer inside).
T2 [˚C]: temperature of first concrete layer (second layer inside).
External
wall from
inside to
outside
direction
Thickne
ss
[mm]
Thermal
conductivit
y W/make]
Specific
heat
capacity
[J/chg.]
Density
[kg/m3]
Wood 5 0.17 2000 700
Concrete 25 1.7 920 2300
Brick 450 0.8 800 1700
Concrete 25 1.7 920 2300
Table 2: Dimensions of external wall
By rearranging equation (5), T1 can be calculated as
follows:
=
(
1
1
ℎ 2.
( – ) −
2. .
. .
( − ))
………… (6)
From the above equation temperature of next layer can be
calculated given by [6]
. .
. .
. .
( )
. .
. .
( )….
(7)
Where:
ρ2 [kg/m3]: density of first concrete layer (second layer
inside).
cp2 [J/chg.]: specific heat capacity of first concrete layer
(second layer inside).
V2 [m3]: volume of first concrete layer (second layer
inside).
k3 [W/make]: thermal conductivity of brick layer (third
layer inside).
l3 [m]: thickness of brick layer (third layer inside).
A3 [m2]: area of brick layer (third layer inside).
T3 [˚C]: temperature of brick layer (third layer inside).
By rearranging equation (7), T2 can be calculated as
follows
=
(
. .
. .
( − ) −
. .
. .
( − )) / ……. (8)
Next layers temperature must be calculated for balancing
the brick layer and rearranging the equation we get
. .
. .
. .
( )
. .
. .
( )
……. (9)
Where:
ρ3 [kg/m3]: internal third layer density
cp3 [J/chg.]: third layers specific heat
V3 [cubic mt]: Volume of the third brick-based layer
k4 [W/mt]: second concrete layer i.e.., fourth layer thermal
conduction
l4 [mt]: fourth layer thickness
A4 [sq mt]: fourth layer area
T4 [˚C]: fourth layer temperature
By rewriting the above equation (9), T3 can be defined as
mentioned:
= (
. .
. .
( − ) −
. .
. .
( − )) /
…….(10)
Figure (2): Effect of radiation on external walls
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4. C. Model of heat transfer through Window
Windows internally transfer heat which is its basic property
given by
Qwindows=Awindows.Uwindows. (Tso,windows-
Tindoor)+I.SC. Awindows …. (11)
Where:
Qwindows [W]: transfer of heat through window
Awindows [m2]: area of described window
Uwindows [W/m2. K]: heat transfer coefficient
Tso, windows [˚C]: Solar – air temperature of the window with
assumed radiation value of 0.1.
Tindoor [˚C]: indoor temperature.
I [W/m2]: incident beam radiation on the window
SC [-]: sun heat coefficient
D. Ventilation heat transfer analysis:
Heat gain of Ventilation model for the described building
is evaluated as
=
{ . . . ( – )/
3600 … (12)
Where:
Q Ventilation [W]: ventilation heat gain and loss
N [1/h]: air change rate = change in air due to ventilation
and infiltration.
Infiltration for the discussed room is assumed to be 0.2
1/h.
Vindoor [m3]: volume of indoor spacing.
Cp air [J/chg.]: air heat capacity.
Toutdoor [K]: external temperature (outside)
Tindoor [K]: internal temperature (inside).
V. OPTIMIZATION PREFERRED
TARP is a thermal analysis research program developed
as research tool for analyzing thermal conditions of
buildings. It specifically aimed to study the integration of
various heat transfer criteria. TARP uses the
comprehensive heat balance technique for the
synchronized calculation of the energy necessities of
several room structures. Internal convection and
conduction processes are simulated in systematic
procedure. Programs basic reference instruction booklet
describes the designed algorithm, initial, final output, and
desired program outcome of TARP. The program is
modified to portable nature [8,9]. It is compiled with
FORTRAN 77 and runs on CDC and UNIVAC
environments. Expansions of the future programming are
anticipated, specifically for the concurrent simulation of the
equipment parametric performance and thermal analysis of
the building. Also, features which would have enhanced the
usability of the program for building designers and
engineers have given way to research features [10,11].
TARP provides more detailed models for air movement and
multi room analysis. It is also more portable and easier to
modify. TARP algorithm calculates the convective heat
transfer coefficients depending on the difference between
the surface and mean air temperature. These coefficients
for floor /ceiling of building structure are designed in
energy plus with the help of TARP algorithm. Energy Plus
explicitly models radiation between surfaces, and so a
radiation film coefficient does not need to be provided [10].
For convection, Energy Plus has several algorithms
available. In Energy Plus, the (TARP) algorithm is used for
interior and exterior surfaces. Energy Plus includes
enhanced heat transfer algorithms [7] and as well as several
new ground temperature models. Preliminary connections
to these models have been made, and final conclusions are
drawn at this time.
VI. RESULTS
In this work we introduce a novel optimization process
that is able to implement the concept through a holistic
optimization providing rapid and comprehensive analysis
on the cost-optimality of ZEB. Simulation model was
developed in Energy Plus and the study allows widely used
applications.
Figure (3): Simulation of Building using Sketch up
Electricity
Intensity
[MJ/m2
]
District
cooling
Intensity
[MJ/m2
]
District
Heating
Intensity
[MJ/m2
]
LIGHTING 2337.04 0 0
HVAC 0 1155778.2 2580.65
OTHER
LOAD
108.37 0 0
TOTAL 2445.41 1155778.2 2580.65
Table 3: Electrical utilization in the building
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5. Figure (4): Utility used /total floor Area
Figure (5): Simulation control of Total site
Figure (6): People Nominal Internal Gains
Figure (7): Light Internal Gains
Figure (8): Zone Ventilation Airflow
Figure (9): Design Day Data
Figure (10): Summary of Envelope
Figure (11): Lighting Load summary
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6. Figure (12): Amount of Day light controlled
Table 4: Avg Outdoor air occupied
Table 5: Minimum Outdoor air for number of
occupants
Figure (13): Annual and Peak Values of Building
Figure (14): Heat Gain Summary
Figure (15): Schedules
Figure (16): Electricity Peak Demand of year
Figure (17): Cooling demand of annual building
consumption
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7. VII. DISCUSSION OF RESULTS
Based on the above mathematical equations and
dimensions a building was designed and developed using
Sketchup and was shown in the figure (3). Now the given
building was incorporated with open studio and energy plus
software by embedding TARP optimization. As a result, the
total approximated electrical utilization for the year 2019
was shown in table (3).The results using TARP provided
an optimized way of electrical utilization and the rate of
change of heat intensity in a building for the particular
HVAC load was depicted in figure (4).Figure (5) shows the
control of total site by considering the various parameters
like building surface, ground temperature, daylighting,
shadow effect and ground reflectance throughout the year.
Figure (6) depicts the amount of internal gains that can be
obtained in the building for the considered occupants/
square feet. Here the TARP algorithm was helpful in
providing the early prediction of internal gains in both of
the rooms. Figure (7) shows the rise of internal gains in
both the rooms where figure (8) explains the rate of airflow
through the ventilation for the considered year. As the
simulation was carried out for a commercial building which
will be in operation for 5 days a week.so neglecting the
non-working days the design day under Bhubaneswar zone
(nearer to gunupur) was depicted in figure (9). The
summary of various surfaces and its reflection was obtained
in figure (10). Figure (11) shows the total lighting load in a
building and figure (12) explains the total power that can
be saved in a year by adopting daylight control. Where the
remaining tables 4 ,5 and figures (13,14) explains the total
working hours in the building and the total load acting on
that time for the number of occupants and heat gain
summary for the considered occupants. Figure (15) shows
the planned schedules of the operating hours for both the
rooms and figures (16,17) predicts the peak and cooling
electricity demand for the entire year with its energy
performance.
VIII. CONCLUSION
In recent times construction of buildings are growing
into zero energy buildings as they play a major role in
reduction of energy consumption. So here a building was
designed and developed by using Sketch up, Open studio
and Energy-plus by incorporating TARP optimized
algorithm to view for some better outcomes when
compared to a normal building. Here the simulation was
done by considering the design data of Bhubaneswar
(Nearby zone to Gunupur) and the best optimum results
were obtained as well as validated. The results clearly
showed that the electricity consumption varied from
month-month and their load profiles were also displayed
successfully with the heating and cooling demands. Apart
from the simulation, the building envelope and its layers
were estimated by the help of mathematical modeling.
Also, respective dimensions were used in simulation and
the optimized results are displayed successfully with better
efficient values when compared to the normal building
parameters.
Finally, the simulation delivered the early
prediction of the electricity usage in the building and
illustrated the advantage of preferring daylighting control
in the room using the real time weather conditions of
energy plus. Here the TARP optimization was favored to
run the simulation and it gathered the data with the
optimum values to design the building and observed the
best results before constructing the building physically
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