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American Journal of  Environmental
Economics (AJEE) 

Barriers to Adoption of  Energy-Efficient HVAC Systems in Middle-Class Households in 
Pakistan

Muhammad Awais Javed1*, Usman Latif2, Muhammad Sajjad3

Volume 4 Issue 1, Year 2025
ISSN: 2833-7905 (Online)

DOI: https://doi.org/10.54536/ajee.v4i1.5433
https://journals.e-palli.com/home/index.php/ajee

Article Information ABSTRACT

Received: May 22, 2025

Accepted: June 30, 2025

Published: August 25, 2025

Buildings and additional structures utilize a significant helping of  the energy in the sphere. 
HVAC systems are mostly responsible for building energy use in middle class households 
in Pakistan. This study proposes a simulation-based optimization approach for efficiently 
measuring HVAC systems choices in the early design stages to get the optimal configuration 
in middle class households in Pakistan. This strategy is applied establishing HVAC systems 
in middle class households in Pakistan. The ventilated Dunkel cycle performs better than the 
other two designs for high COP systems in all three climate zones; nevertheless, ventilation 
is the superior choice in middle class households in Pakistan. Systematic simulations can help 
with the difficult process of  optimizing HVAC systems designs in middle class households 
in Pakistan. This invention will assist HVAC systems design are selecting the optimal systems 
configuration and design parameters during the early design phase by accounting for various 
middle-class households in Pakistan. 

Keywords

Energy Systems, HVAC System, 
Middle Class Households’ 
Buildings, Pakistan,Ventilated 
Dunkel Cycle

1 USchool of  Business, Los Angeles, International American University, Main Campus Los Angeles, 90010, CA, USA
2 Head of  Engineering Department, Bunny’s Limited Lahore Pakistan
3 Head of  Operations, AJRD Organization Lahore Pakistan
* Corresponding author’s e-mail: dr.mawaisjaved@gmail.com

INTRODUCTION
As global temperatures rise, Pakistan is experiencing 
longer and more intense heat waves. Middle class 
households increasingly rely on HVAC systems for 
cooling, leading to higher electricity consumption, 
frequent power shortages, and growing carbon emissions. 
Although energy-efficient HVAC systems technologies—
such as inverter air conditioners, smart thermostats, 
and solar-assisted cooling systems—are available, 
their adoption remains limited among middle-class 
households. Despite their potential for reducing energy 
bills and environmental impact, energy-efficient HVAC 
systems are not widely adopted in Pakistan’s middle-class 
neighborhoods. Optimization of  traditional setups and 
introduction of  efficient innovation of  HVAC systems 
utilizing renewable energy resources is one approach 
to the problem (McQuiston et al., 2000). Most of  the 
expansion is taking place in developing nations that are 
not members of  the OECD, particularly in Asia. Pakistan 
and other developing nations accounted for 44% of  total 
energy consumption in 2006–07. By 2030, home energy 
use will have nearly doubled and accounted for the 
lion’s share of  total energy consumption. However, by 
2030, it could only account for 32% of  total final energy 
in middle class households (Ellis & Mathews, 2002). 
Growth in population, enhancement of  comfort demand, 
global climate change, and time spent inside buildings 
anticipated the growing trend of  energy consumption 
in building industry. The middle-class household’s 
residential energy demand is anticipated to expand at 
an average pace of  1.1% per year from 2008 to 2035. 
Similarly, the growth in commercial sector is predicted 
to expand at an average rate of  1.5% each year from 

2008 to 2035 (EIA, 2012). Therefore, due to increasing 
energy needs, pricing, and environmental challenges, 
the developed nations are focused on buildings sector 
as the biggest opportunity for energy savings (BPIE, 
2011). The buildings energy demands are the major 
cause of  considerable rise in the power consumption 
due to rising space heating, cooling, ventilation, and 
refrigeration requirements in middle class households 
in Pakistan (IEA, 2009). Energy usage in buildings is 
closely associated with energy needs of  HVAC systems in 
middle class households in Pakistan. HVAC is the greatest 
energy end use both in the residential and non-residential 
sector. The air-conditioning is responsible for 10% to 
60% of  the overall building energy usage, depending on 
the building type (Ellis & Mathews, 2002). In developed 
countries, HVAC systems are the most energy consuming 
devices, accounting for about 10–20% of  final energy 
use (Perez-Lombard et al., 2008). The HVAC systems 
configuration is a conceptual design of  HVAC systems 
including the active components, airflow set-up, and the 
control strategies with set points. Selection of  HVAC 
systems configuration is typically decided in the early 
stage of  the design process in middle class households 
in Pakistan. The design phase of  heating, ventilation, and 
air conditioning HVAC systems in a new building facility 
presents the greatest opportunity for energy savings. 
When compared to the cost of  upgrading an old building 
with an efficient HVAC system, it is typically more cost-
effective to install energy-efficient heating, ventilation, 
and air conditioning business equipment during the 
construction of  a structure (Galitsky, 2007). This study 
seeks to identify the economic, social, technical, and 
informational barriers that prevent widespread uptake 



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in middle class households in Pakistan. The study is 
investigating the economic, cultural, and informational 
factors which are used to prevent households from 
switching to efficient cooling technologies in middle 
class households in Pakistan. To identify key factors 
influencing consumer choices in HVAC systems among 
urban middle-income households. To analyze barrier to 
adopt energy-efficient HVAC technologies in middle class 
households in Pakistan. To assess awareness levels about 
energy-efficient options and long-term cost savings. To 
propose targeted policy and market-based interventions 
that could boost adoption in middle class households in 
Pakistan. This study addresses the intersection of  climate 
adaptation, energy equity, and behavioural economics, 
offering practical solutions to reduce energy consumption 
and improve resilience to rising temperatures in middle 
class households in Pakistan. In order to evaluate the 
suggested strategy for the selection of  optimal HVAC 
systems configuration through the use of  a real chilled 
water systems, the goal is to establish an incremental 
simulation-based optimization methodology for the 
purpose of  designing a chilled water system. The study 
provides the literature review to resolve the cost concerns, 
trust in new tech, lack of  awareness, and installation 
challenges in middle class households in Pakistan. The 
results reveal that the clear insight into why middle-class 
households hesitate to switch to efficient cooling systems. 
Recommendations for policy makers, energy companies, 
and NGOs to design targeted incentives in middle class 
households in Pakistan. 

LITERATURE REVIEW
Modelling techniques for HVAC components, modelling 
approaches for HVAC control, and modelling approaches 
for HVAC systems are the three categories that may be 
used to classify the many modelling approaches for HVAC 
in middle class households in Pakistan. In addition, the 
methodologies for solving the HVAC systems simulation 
model is categorized as simultaneous modular solution, 
independent modular solution, and equation-based 
solution employing manipulation (Trcka & Hensen, 
2010). Within the realm of  HVAC modelling, there 
were a number of  studies conducted at the component, 
control, and systems levels in middle class households 
in Pakistan. At the component level, models of  air- and 
water-cooled chillers were constructed in TRNSYS in 
order to analyze the performance of  these chillers using 
a variety of  control techniques (Wang et al., 2004). In 
a similar manner, simpler models of  cooling coil units 
(Jin et al., 2007) and cooling towers (Wu et al., 2007) 
were constructed for the purpose of  controlling and 
optimizing HVAC systems in middle class households 
in Pakistan. In another study, component models of  an 
axial fan, air filter, and duct for a ventilation unit were 
constructed in Simulink for the purpose of  analyzing 
the performance of  the constant airflow management 
scheme (Bertagnolio et al., 2008). At the systems level, a 
combined building-HVAC systems model was provided. 

This model included representations of  both the building 
zone and the HVAC equipment. It was demonstrated 
that the model might be useful for conducting energy 
audits of  commercial buildings (Beccali, 2008). In order 
to assess the efficacy of  desiccant and solar cooling 
systems, a number of  desiccant wheel models were 
created. To forecast the performance of  three distinct 
kinds of  desiccant wheels made with various kinds of  
solid desiccants, a psychrometric model for desiccant 
wheels was created (Nia et al., 2006). Additionally, a 
modelling and simulation method for desiccant wheels 
was introduced in Simulink for a parametric analysis of  
desiccant wheels. Simple correlations between the outlet 
air conditions and physically quantifiable input variables 
were developed using the modelling solutions (Haddad 
et al., 2008). Another research showed simulation models 
of  a hybrid HVAC systems that combined a desiccant 
cooling systems with a traditional vapour compression 
system. The hybrid systems’ ability to reduce power was 
examined using the models in middle class households 
in Pakistan (Zhang, 2005). Typically, each simulation’s 
static variables are set systems design parameters. These 
kinds of  issues include the design of  building envelopes, 
HVAC systems and components, ducting and hydraulic 
systems, and lighting in middle class households in 
Pakistan (Zogou & Stamatelos, 2007). Over the past 10 
years, a lot of  research has been done on optimization 
approaches based on HVAC models. Transsystemsic was 
used to optimize the thermal performance of  a ground 
source heat pump systems in order to lower the energy 
expenses associated with heating and cooling the building 
in middle class households (Zogou & Stamatelos, 2007). 
HVAC systems were also optimized and controlled in 
real time using the created cooling coil unit and cooling 
tower models (Bertagnolio et al., 2008). In a similar vein, 
established component models were used to optimize the 
entire HVAC systems globally (Lu et al., 2005). For the 
best water-cooled chiller and cooling tower combination, 
another optimization study was conducted in middle class 
households. Condenser water flow rate, cooling tower 
approach, and wet bulb design were identified as critical 
characteristics for maximizing systems life cycle costs and 
performance (Furlong & Morrison, 2005). Comparing 
the packed direct expansion strategy to traditional 
control measures, significant savings were expected (Huh 
& Brandemuehl, 2008). A particle swarm optimization 
technique was used to solve an integrated energy 
optimization model of  an HVAC systems in middle class 
households (Kusiak et al., 2010). An additional systems 
optimization using a dynamic neural net study led to a 
30% reduction in energy consumption in middle class 
households HVAC systems (Kusiak & Xu, 2012).

MATERIALS AND METHODOLOGY
To address various characteristics in the initial design 
phases and reduce energy requirements, model-based 
systems evaluations are extensively employed, especially 
in the construction industry in middle class households 



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in Pakistan. Utilizing appropriate model-based simulation 
and optimization tools facilitates the evaluation of  various 
HVAC systems solutions in middle class households in 
Pakistan. 

Modelling and simulation of  HVAC systems designs
An automated simulation-based optimization method 
is introduced in this study to automatically choose the 
best HVAC systems configuration. The systems deliver 
high-quality indoor conditions with minimal expense 
and environmental effect when configured optimally 
in middle class households in Pakistan. Modelica/
Dymola is used to develop the equation-based object-
oriented modelling and simulation technique, and 
the combination of  Modelica/Dymola with GenOpt 
guarantees the automated selection of  the best HVAC 
systems. There are major and secondary components 
in the HVAC systems model. A “component model” 
in building systems simulation is a computer model of  
a basic HVAC systems in middle class households in 
Pakistan. Each component model in Modelica is visually 
represented by an icon made up of  a collection of  
distinct equations. Additionally, each component model 
represents a real HVAC (cooling tower, boiler, chiller) 
business device model with physical interface ports to 
connection them to other component models. In order to 
represent an entire HVAC system, different components 
are coupled together via these interface ports in middle 
class households in Pakistan. Various formations are 
included in the overall HVAC systems model. The design 
specifications, which specify the group of  parts and their 
relationships, serve as the foundation for the model. The 
generated model’s component models are all precisely 
proportioned to satisfy the necessary building load 
requirements. To mimic their performance in terms of  
energy consumption, the model’s potential configurations 
are then changed based on a predetermined criterion.

Inputs of  HVAC Systems Model
Design load and cooling/heating load profiles are 
important for HVAC systems optimization and 
assessment. The building’s load profile specifies the load’s 
temporal fluctuation, whereas the design load details the 
total installed systems capacity, which includes cooling 
towers, chillers, pumps, and pipes. In addition, important 
HVAC systems in particular need a load profile for 
effective systems design and staging. All of  the design 
decisions, such as the chillers’ unloading strategies, the 
cooling towers’ and pumps’ use of  variable frequency 
drives, and the equipment’s relative sizes, are part of  the 
staging process. Climate, operating hours, base loads, and 
other variables all have an impact on the building load 
profile in middle class households in Pakistan. A number 
of  factors, including external factors, the building’s 
exterior, internal heat gains, and ventilation needs, go into 
estimating peak load demands in middle class households 
in Pakistan. The peak load needs are determined using 
a variety of  methods, including calculations/simulations, 

site measurements, and general guidelines in middle class 
households in Pakistan.

Techniques for creating a model of  HVAC company 
systems configurations
The primary goal of  this research is to develop a 
method for automatically choosing the best HVAC 
systems configuration during the design phase in middle 
class households in Pakistan. Various HVAC systems 
configurations will be evaluated as part of  the job. 
Consequently, it is critical to create a physical model of  
the systems that can mimic various HVAC systems in 
middle class households in Pakistan. Furthermore, for 
optimal performance at both the systems configuration 
and design levels, the model should be able to change 
the critical design parameters of  the component 
models simultaneously. Two options for creating such a 
systems model were identified following an exhaustive 
examination of  Dymola/Modelica in middle class 
households in Pakistan.

Initial Approach: Declaring component models 
conditionally
Models of  individual subsystems make up the HVAC 
systems as a whole. The many physical components of  
an HVAC systems, like as pumps, chillers, boilers, and 
cooling towers, are each represented by a separate model. 
The development of  the overall model is accomplished by 
carefully linking these sub-component models together 
using connectors that extend from their individual 
interface ports. The first approach uses conditional 
declarations in the systems model to include sub-
component models. Both the building’s load needs and 
the weather have a role in the conditional declaration in 
middle class households in Pakistan. But, to avoid errors, 
the model as a whole has to keep an even number of  
equations and unknown variables throughout the process. 
A Modelica function called “read-Real-Parameter” and 
a package called “External Data” are used to apply the 
approach in Dymola/Modelica. Importing the function 
from the Modelica standard library into the overall HVAC 
systems model is necessary for model development in 
middle class households in Pakistan. Along with the name 
of  the external file where these characters are created 
and given suitable values, certain characters need to be 
declared as parameters in the overall model. Such symbols 
could represent HVAC systems design and configuration 
characteristics. It is essential that the external file adheres 
to the specified specifications for the component 
model variation in terms of  configuration and design 
parameters, with appropriate values supplied. The right 
usage of  these configuration parameters with each sub-
component model using an if  statement followed by the 
suitable logical condition is necessary for the conditional 
declaration of  sub-component models. 

Systems Sizing for HVAC Systems
Systems configuration and sizing are closely connected in 



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HVAC systems in middle class households in Pakistan. The 
HVAC systems model and its component models must be 
correctly designed to meet building load needs in middle 
class households in Pakistan. The current study’s HVAC 
systems configuration optimization technique may change 
systems configuration and design parameters. This paper 
proposes two techniques to connect configuration and 
size problems during HVAC systems model development. 
Any component model in Modelica can have variable 
component sizes by changing its design parameters. The 
HVAC systems setup and sizing approach depends on 
how component design characteristics may be modified. 
The first technique is to create an HVAC systems model 
that uses the same component model in different sizes for 
a specific application. The ideal component size may be 
established by simulating all component model sizes. If  
a component model (CM) comes in three sizes, it will be 
used three times in the systems model. For an application, 
all three choices are simulated, and the CM with optimum 
performance displays the ideal component size and is 
selected. One CM can be employed in the systems model 
in the second method. Creating a record of  all three 
component sizes allows for implementation. To find the 
best component size, the sizing choices can be connected 
to the CM repeatedly throughout simulation.

Simulation of  HVAC Systems Model
The development of  an HVAC systems model involves 
implementing a way to automatically modify systems 
settings in middle class households in Pakistan. The 
simulation technique involves model experiments to 
anticipate its behaviour under actual settings. This study 
uses Dymola/Modelica for HVAC systems modelling and 
simulation. Dymola is powerful for charting, animation, 
and experimentation. It has two modes: modelling 
for systems model creation and simulation for model 
experiments. Simulation mode includes setup, plot, 
animation, and variable browser. The simulation setup has 
three basic groups: simulation, output, and integration.

Refining the Setup of  Cooled Water Systems 
The majority of  the energy consumed by HVAC systems 
is produced by the chilled water systems, which comprise 
the main components of  these systems, including 
chillers, cooling towers, and pumps. The potential for 
HVAC systems to save energy may be greatly increased 
by optimizing chilled water during the early design stage. 
But optimizing a chilled water systems isn’t a simple 
feat, especially when it comes to optimizing the systems’ 
configuration or architecture. The method becomes more 
intricate when both tiers are used together. Moreover, 
the research details a strategy for optimizing chilled 
water systems designs that has undergone incremental 
evolution. Before optimizing the systems design in its 
entirety, the best practice design criteria are confirmed 
by first experimenting with the configuration parameters 
of  the systems under fixed design conditions. In order 
to determine the best configuration for the systems, the 
simulation-based optimization method combines the 
GenOpt generic optimization tool with the dynamic 
modelling and simulation application Dymola/Modelica. 
In order to test and replicate various chilled water systems 
setups, a dynamic systems model is created. Using five 
design factors, the chilled water systems may be optimized 
at both the design and configuration levels. The layout 
of  the systems is affected by two independent variables: 
the quantity of  chillers and cooling towers. The demand 
for building loads, the temperature differential across the 
condenser, and the speed of  the cooling tower fan are 
three other continuous factors that are relevant to the 
design of  the systems.

Description of  the Chilled Water Systems
The systems are comprised of  three chillers of  identical 
size, each with a cooling capacity of  2725 kW (775 tonnes), 
as shown in Table 1. Five identically sized draw-through 
cross-flow cooling tower cells, each with a capacity of  
around 76 l/s (1200 gpm), make up the systems.

Table 1: Specifications of  the investigated chilled water systems
Compressor type Centrifugal 
Nominal cooling capacity kW [tons] 2725 [775] 
Nominal compressor power kW [tons] 446.4 [127] 
Minimum cooling capacity kW [tons] 457 [130] 
Design COP 6.1 
Design chilled water supply/return temperature oC[oF] 6.7/12.2 [44/54] 
Design chilled water flow rate l/s [gpm] 57 [900] 
Design condenser water entering temperature oC[oF] 29.4 [85] 
Design condenser water flow rate l/s [gpm] 111[1760] 
Cooling towers: 
Type Draw-through 
Water flow rate l/s [gpm] 76 [1200] 
Fan motor power kW [hp] 18.65 [25] 
Design wet bulb temperature oC[oF] 17 [62.6] 



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Design dry bulb temperature oC[oF] 26 [78.8] 
Design approach temperatures oC[oF] 8.3[15] 
Design range temperature oC[oF] 5.56 [10] 
Pumps: 
Rated power of  each chilled water pump kW [hp] 30 [40] 
Rated power of  each condenser water pump kW [hp] 19 [25] 
Design range temperature oC[oF] 5.56 [10] 

In a headered configuration, three condenser water 
pumps of  identical capacity are connected; each pump 
may service one of  the chillers or tower cells, and their 
combined flow rate is 111 l/s (1760 gpm). The motors 
powering the pumps are 19 kW (25 hp). Also headered 
are three chilled water pumps of  identical size, each with a 
capacity of  57 l/s (900 gpm) and 30 kW (40hp). Standard 
550/590-2003 of  the Air Conditioning and Refrigeration 
Institute (ARI) and the test conditions established by 
the Cooling Tower Institute (CTI) form the basis of  the 
design conditions in middle class households in Pakistan. 
The cooling tower model is based on the ASHRAE 

standard 90.1-2004 climatic data for San Francisco, which 
states that the design wet bulb temperature is 17oC [63oF] 
and the dry bulb temperature is 26oC [78.8oF].

Constructing Systems Economically
Table 2 details the startup and installation costs of  water-
cooled centrifugal chillers and cooling towers, as well as 
the cost of  piping, fittings, and valves. The computations 
take into account an anticipated 25% contractor markup. 
Costs associated with chillers, cooling towers, pipes, 
labour, fittings, and valves needed for effective hydronic 
design make up the initial cost for each setup.

Table 2: Summary of  initial costs
Description Cost/ton € [$] Total unit cost € [$] 
Water-cooled centrifugal chiller (Nominal 775 tons each) 147 [180] 113925 [139500] 
Chiller installation cost 37 [45] 28370 [34875] 
Cooling Tower (Nominal 400 tons each) 106 [130] 42400 [52000] 
Cooling tower installation cost 4.1 [5] 1640 [2000] 
Piping/Fitting/Valve 
a chilled water side --- 13015 [16000] 
b condenser water side --- 19523 [24000] 
c adding another chiller in the systems --- 4393 [5400] 
d adding another cooling tower in the systems --- 2440 [3000] 
Contractor Markup 25% 
Estimated total baseline cost for 1 chiller and 1 cooling tower 273591[335469] 
Estimated total cost for adding each chiller 183360 [224719] 
Estimated total cost for adding each cooling tower 58100 [71250] 

Nevertheless, the Pipe Size Optimization tool is used 
to estimate the cost of  the chilled water system pipes, 
fittings, and valves. Based on the flow rate for certain 
piping segments, the tool determines the initial cost of  
the pipe. Also included are the typical types and quantities 
of  valves and fittings utilised in chilled water systems. 
Additionally, we supply the prices of  various fittings and 
valves according to the pipe size. Based on the reference 
systems piping design, the quantity and kind of  valves 
and fittings are determined in this investigation the 
HVAC systems in middle class households in Pakistan.
 
Optimization Procedure
Total systems power consumption is the goal function in 
the five-variable design process that optimizes the chilled 
water systems as a whole. The quantity of  chillers (CH) 

and cooling towers (CT) are two independent design 
factors. Building load demand (Qload), temperature 
differential across the condenser (ΔT), and cooling tower 
fan speed (F) are three design factors that are constant. 
The variables’ bounds are displayed in Table 3.
A direct search Hooke-Jeeves (HJ) method and a stochastic 
population-based constriction coefficient algorithm 
make up the hybrid global optimization algorithm. The 
main benefit of  this algorithm is that, during the global 
PSO search, the likelihood of  approaching the global 
minimum is increased, as opposed to only reaching a local 
minimum, and the search is then refined locally by the HJ 
algorithm in middle class households in Pakistan. Table 
4 summarizes the GPSPSOCCHJ algorithm parameters 
that were used for the current investigation.



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Table 3: Design variables and boundaries
Tower Fan Speed, F 
(%) 

Temp. difference 
condenser side, 
ΔT (oC) [oF]

No. of  chiller, CH No. of  cooling 
towers, CT 

Building load 
Qload 

Minimum 0.3 (kW) [Tons] 1 3 1055 [300] 
Maximum 1 15 [27] 3 18 7032 [2000] 
Step 0.01 0.01 1 1 
Initial 1 3 1 3 

Table 5: Optimal values of  Ptotal and energy use of  all strategies (minimum values highlighted)
Qload (kW 
[tons]) 

1st Strategy Ptotal 
(kW) kW/ton 

2nd Strategy 
Ptotal (kW) kW/ton

3rd Strategy 
Ptotal (kW) kW/ton

Percentage of  
power saving (%)

7032 [2000] 2757.3 1.37 1808.4 0.9 1557.3 0.78 43.5 
5274 [1500] 1498.8 0.99 1178.8 0.78 993.1 0.66 33.8 
3516 [1000] 752.7 0.75 706.9 0.71 582.7 0.58 22.6 
2461 [700] 479.7 0.68 479.7 0.68 394.9 0.56 17.6 
1582 [450] 297.8 0.66 297.8 0.66 240.7 0.53 19.2 
1055 [300] 207.4 0.69 207.4 0.69 171.6 0.57 17.3 

Table 4: Optimization algorithm input parameters
Parameters Value
Neighborhood topology von-Neumann
Neighborhood size 5
Number of  particles 20
Number of  generations 5
Seed 1
Cognitive acceleration 2.8
Social acceleration 1.3
Max velocity gain continuous 0.5
Max velocity discrete 4
Constriction gain 0.5
Mesh size divider 2
Initial mesh size exponent 0
Mesh size exponent increment 1
Number of  step reductions 4

First, a baseline system with no more than five cooling 
towers and a fixed design temperature difference across 
the condenser at full fan speed; second, a modified 
systems with an increased number of  cooling towers 
according to the flow turndown limit; third, a modified 
systems with varying systems design and configuration 
parameters; and finally, a methodology is proposed for 
design optimization of  the chilled water systems at the 
initial design stage in middle class households HVAC 
systems. A systematic method for optimizing chilled water 
systems designs as a whole is defined by the third strategy, 
whereas the previous two only validate the simulation 
models and validate the best practices in the field. The 
techniques take into account the upfront expenses of  
equipment, such as cooling towers, pipes, fittings, and 
valves. It is worth noting that adding more cooling towers 
and chillers usually lowers the yearly energy costs and 
payback period. According to Table 5, the best methods 
for the whole systems in terms of  power consumption 
and energy usage (in kW/ton) are shown.

The energy consumption figures (kW/ton) are in close 
accordance with the usual figures for chilled water systems. 
The improved systems with variable fan speed and 
temperature differential across the condenser achieves the 
lowest total power consumption and energy utilization. 
Choosing the right mass flow rates and temperature 
variations across the condenser is also crucial for chilled 
water systems performance. To minimize overall systems 
power consumption, the optimization method based 
on simulations improves decision-making on the best 
change of  these parameters. When running chilled water 
systems at their optimal design and configuration settings, 
significant power savings of  17% to 43.5% relative to 
the baseline situation are possible. This research used an 
EOO methodology based on open-source component 

libraries to build a model of  a chilled water systems. 
In order to optimize HVAC systems, it was necessary 
to experiment with different configurations and design 
characteristics of  individual components in middle class 
households HVAC systems. 

CONCLUSIONS
From 2009 to 2035, the global demand for energy is 
expected to rise by over 50%, continuing a trend that 
began a few decades ago in middle class households 
in Pakistan. About 21% of  the world’s total energy 
requirements are attributable to buildings, making their 
contribution substantial to overall energy consumption 
in middle class households in Pakistan. In middle class 
households in Pakistan account for around 41% and 



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40% of  overall energy use. In contrast, buildings account 
up almost 44% in developing nations like Pakistan. The 
HVAC systems that keep the intended comfort levels 
are the primary contributors to the energy needs of  
the building. Consequently, there is a lot of  room for 
improvement in the building sector’s energy efficiency 
through the optimization of  HVAC systems. A great 
deal of  study is on improving HVAC systems. This study 
optimizes HVAC systems in all their facets by conducting a 
thorough literature evaluation in middle class households 
in Pakistan. Based on what we can tell from the literature 
survey, most research focusses on optimizing individual 
components or operational controls at the systems level, 
rather than systems configurations analysis. Additionally, 
the best HVAC systems configuration could not be 
determined using any kind of  systematic method in 
middle class households in Pakistan. One of  the most 
important things to consider during the first design phase 
is the ideal configuration of  the HVAC systems, as this 
has a major impact on the total energy needs of  the 
building in middle class households in Pakistan. When 
deciding on a systems design for an HVAC, it is important 
to consider not only the HVAC systems’ economic and 
environmental aspects, but also the systems type, the 
kind and quantity of  components, and operational 
control techniques. Various configurations for diverse 
applications are being developed using components 
of  the HVAC systems in middle class households in 
Pakistan. Innovative and alternative systems designs 
make use of  renewable energy resources, such as solar air 
conditioning and desiccant cooling in a variety of  forms 
in middle class households in Pakistan. The difficulty of  
deciding on a suitable design for a given load demand in 
a given environment was demonstrated by all of  these 
setups. Assessment of  HVAC systems setup and design 
choices in practice is reliant on the designer’s expertise 
and experience in middle class households in Pakistan. 
However, the knowledge and abilities of  a person or 
team are sometimes constrained by the competence of  
the systems that are created. Consequently, in order to get 
the best possible automated selection of  HVAC systems 
configuration, a method is created and tested that is based 
on systematic optimization in middle class households 
in Pakistan. The created method relies on Dymola/
Modelica, an environment for EOO modelling and 
simulation that is built on equations. A number of  HVAC 
systems’ physical component models make up the EOO 
modelling and simulation in middle class households in 
Pakistan.

Future Research Suggestions
Based on study and results, the following areas suggest 
ways to improve the suggested technique. Dymola/
Modelica’s HAVC systems component models cannot 
analyze all HVAC systems combinations in middle class 
households in Pakistan. Additional contributions are 
needed to improve the Modelica component library. 
Thus, such models must be updated to estimate systems 

performance in middle class households in Pakistan. 
Second modelling requires additional study to upgrade 
Modelica language to automatically modify component 
model classes. Each component class change also affects 
the parameter assigned values, requiring them to be 
declared afresh in middle class households in Pakistan. 

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