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Selection of Inverter Technology Air Conditioner: An Evaluation Based on Distance from Average Solution

Authors:
  • Guru Nanak Dev Engg College Ludhiana affiliated to Punjab Technical University

Abstract and Figures

The Evaluation Based on Distance from Average Solution (EDAS) technique based on the multi-criteria decision-making method (MCDM) is utilized in the present work. Different air conditioners (AC) based on inverter technology have been assessed as per user requirements with sustainable attributes. The AC is supplied by various brands that have several features connected with it, like cost, power input, airflow, etc. The new market accounts for a wide variety of consumer requirements. Therefore, offering a precise technique to pick the best alternative for buyers/retailers/wholesalers to meet their obligations is the need of the hour. The data of different ACs based on inverter technology were collected for decision making. The Equal weights method is utilized to allocate weights of significance to the attributes. The ranks obtained with EDAS having identical weights are displayed. The presented technique is handy for buyers/retailers/wholesalers. Further, the different weighting techniques and more alternatives can be considered.
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17th – 19th September, 2020
International Congress
SUSTAINABLE DEVELOPMENT
THROUGH
ENGINEERING
INNOVATIONS
www.groupexcelindia.com
ExcEl IndIa PublIshErs
nEw dElhI
17th – 19th September, 2020
Editors
Dr. Harmeet SingH
Dr. arvinD DHingra
Dr. Sumeet Kaur SeHra
GURU NANAK DEV ENGINEERING COLLEGE, LUDHIANA
(An Autonomous College u/s 2 (f) and 12 (B) of UGC Act, 1956)
Ludhiana, Punjab (India)
International Congress
SUSTAINABLE DEVELOPMENT
THROUGH
ENGINEERING
INNOVATIONS
First Impression: August 2020
© Guru Nanak Dev Engineering College, Ludhiana
International Conference on Sustainable Development through Engineering Innovations
ISBN: 978-93-89947-14-4
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Managing Business in VUCA World: Cases and Experiences
ISBN: 978-93-86724-02-1
No part of this publication may be reproduced or transmitted in any form by any
means, electronic or mechanical, including photocopy, recording, or any
information storage and retrieval system, without permission in writing from the
copyright owners.
DISCLAIMER
The authors are solely responsible for the contents of the papers compiled in this
volume. The publishers or editors do not take any responsibility for the same in any
manner. Errors, if any, are purely unintentional and readers are requested to
communicate such errors to the editors or publishers to avoid discrepancies
in future.
Published by
EXCEL INDIA PUBLISHERS
91 A, Ground Floor
Pratik Market, Munirka, New Delhi110 067
Tel: +91-11-2671 1755/ 2755/ 3755/ 5755
Fax: +91-11-2671 6755
E-mail: publishing@groupexcelindia.com
Web: www.groupexcelindia.com
Typeset by
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[ v ]
Preface
With the population on earth growing, it has been putting additional burden on the resources. Man
has long been exploiting the naturally available resources to the hilt. To make our living sustainable
on this planet, we need to focus on sustainable development in all spheres of living. With this thought
we have organized the International Conference on Sustainable Development through Engineering
Innovations. Engineers have been at the forefront of finding sustainable solutions whether harnessing
power from sun, re-utilizing the waste generated by civic bodies, reducing the repeated working in
industry etc. This proceeding presented here is a Jist of some of the efforts being made for finding
sustainable engineering solutions to various problems. We are thankful to all authors who have
contributed their research findings for inclusion in the proceedings. The editors are highly thankful
to Management and Principal, Guru Nanak Dev Engineering College, Ludhiana who have been the
driving spirit behind this initiative. We also express our heartfelt thanks to the publishers who bore
with us to produce the proceedings in the present form. We do hope that you shall be benefitted by
this small endeavour of ours and shall do your bit to make our planet sustainable.
Dr. Harmeet Singh
Dr. Arvind Dhingra
Dr. Sumeet Kaur Sehra
[ vii ]
Contents
    Preface v
1. Influence of Industrial Waste on Strength and Durability Performance
of Mortar Mixes
Maninder Singh, Kiran Devi, Amit Kumar and Babita Saini 1
2. Numerical Investigation of Circular Skirted Footings on Sand using
PLAXIS 3D
Gaurav Juneja and R.K. Sharma 6
3. Sustainability of Concrete using Polypropylene Fiber
Sonia, Chandana Boro and Krishna Murari 14
4. Design of Fractal Antenna for Dual Band Applications Based on
Reactive Impedance Surface (RIS)
Harkamaljeet Kaur, Munish Rattan and Balwinder Singh Dhaliwal 20
5. Residential Load Profile Optimization using DSM by Incorporating
Models of Appliances
Karanbir Singh, Kuldeep Singh and Amandeep Singh Ghatora 28
6. Model Reduction of Linear Time Invariant SISO Systems using
Different Optimal Techniques
Parminder Singh, Parag Nijhawan and Arvind Dhingra 36
7. An Ensembled Feature Extraction Approach for Predicting Male
Infertility using Artificial Intelligence
Palak Sood, Sumeet Kaur Sehra and Himani Sharma 43
8. Building a Machine Learning Classifier Model for Diabetes Prognosis
Himani Sharma, Sumeet Kaur, Sehra Kuljit Kaur,
Mandeep Kaur and Palak Sood 53
9. Comparison of Clustering Approaches for Enhancing Sustainability
Performance in WSNSL a Study
Rachna Rana, Yogesh Chhabra and Pankaj Bhambri 62
10. Effect of Various Parameters on Surface Finish and Thickness on ABS
Specimens Printed using FDM Technique
Aniket Yadav, Piyush, Ranvijay Kumar,
Raman Kumar and Jasgurpreet Singh Chohan 72
11. Synthetic Thermal Insulators: Latest Developments
Sarabjit Singh, Suneev Anil Bansal and Shalom Akhai 79
12. Thermal Analysis of Chicken Fiber Augmented Epoxy Composites
Neha Sah, Alka Goel and Arun Kumar Chaudhary 88
13. Selection of Inverter Technology Air Conditioner: An Evaluation
Based on Distance from Average Solution
Rohit Dubey, Raman Kumar,
Paramjit Singh Bilga and Sehijpal Singh 96
Contents
[ viii ]
14. Enigma of Machining Technology Awareness in Manufacturing
Industry of Northern India
Jagdeep Singh and Harpuneet Singh 104
15. Comparative Analysis of Machine Learning Techniques in Effort
Estimation
Ritu, Sumeet Kaur Sehra and Sukhjit Singh Sehra 112
16. Optimization of Power System using Static Synchronous Compensator
to Enhance Voltage Stability
Arshdeep Kaur and Y.S. Brar 120
17. Green, Clean n-Tier Medium Voltage Switchgear
Avanish, Sunil Bhosale, Rahul Hagawane and Priyadarshini 128
18. Sustainable Development in Affordable Highrise Buildings: A Case
Study
Rajesh Kumar, Vanita Aggarwal and Surinder M. Gupta 135
19. Sustainability of Concrete Containing Rubber Aggregates: An
Eco-friendly Approach
Kamaldeep Kaur and Jaspal Singh 142
20. Effect of Sand and Geocell on Bearing Capacity of Pond Ash
Amandeep Singh, Gurdeepak Singh and Anupam Ranikesh Nayal 149
21. A Framework for Denoising Fluorescence Cell Images using Discrete
Curvelet Transform
Sarabpreet Kaur and Harpreet Kaur 158
22. Power Quality Improvement in Distribution System using D-STATCOM
under Various Fault Conditions
Manpreet and Tejinder Singh Saggu 163
23. Harmonic Study of Low Switching Frequency based PV fed 15-Level
and 21-Level Inverters
Vijay Sirohi, Tejinder Singh Saggu and Jagdish Kumar 171
24. Fabrication of Dye-Sensitized Solar Cells based on Natural Dye and
Polymer Gel Electrolytes
Shivani Arora Abrol and Rajeev Sharma 178
25. Bagasse Cogeneration Plant Efficiency Improvement by using Data
Envelopment Analysis Models
Gagandeep Kaur Gill and Rupinderjit Singh 186
26. Speech Parameters Extraction for Text to Speech Synthesis for Punjabi
Navdeep Kaur and Parminder Singh 194
27. Analysis of Machine Translation and its Approaches
Rajneet Kaur and Parminder Singh 202
28. A Comparative Review on Different Methods for Image Captioning
Ankit Nitesh and Jasdeep Kaur 208
29. Artificial Intelligence based Flying Car
Gurmeet Singh, Manpreet Singh and Pankaj Bhambri 216
[ ix ]
Contents
30. Eye Motion Access System using Computer Vision
Kaustav Bora, Gagandeep Singh, Aman Shah,
Prakhar Bhatia and Shikha Gupta 228
31. Process Parameter Optimization of Friction Stir Welding: Effect on
Impact Strength
Kamaljit Singh, Karanbir Singh,
Virat Khanna and Swarn Singh 237
32. Characterization of Injection Molded Coconut Fiber Reinforced
Recycled/ Virgin High Density Polyethylene (HDPE) Composite
Prem Singh, Dharmpal Deepak and V.K. Gupta 248
33. TLBO based Bi-Objective Optimisation in RMS
Kamal Khanna, Gazal Preet Arneja and Rakesh Kumar 256
34. A Privacy Preserving e-Voting System using Blockchain Technologies
Harshpreet Kaur, Jaskirat Kaur,
Jyoti Sah and Manpreet Kaur 263
35. Detection of Plant Leaf Disease using Image Processing and Deep
Learning Technique: A Review
Gagneet Kaur, Priyanka Arora and Jasdeep Kaur 271
36. Utilization of Copper Slag as a Sustainable Building Material
Rajwinder Singh, Karanvir Singh Sohal and Mahesh Patel 284
37. Effect of Soil Structure Interaction on Seismic Response of Buildings
Gorish Dhingra 303
38. AES based Power Efficient Secure Routing for IoT Enabled
Communication
Bhavna Sareen and Arrik Khanna 311
39. High Temperature Erosion Behavior of Plasma Sprayed Al2O3 Coatings
on AISI-304 Stainless Steel
Gaurav Prashar, Hitesh Vasudev and Harmeet Singh 317
40. Optimizing Paneer Production by Reducing Fat Loss in Whey using
Taguchi Orthogonal Array
Sudip Banerjee, N.M. Suri and Sumankant 327
41. Effect of Transmitting Boundaries on Adjacent Structures
Considering SSI
Vijay Kumar, Akash Priyadarshee, Ashish Kumar,
Atul Kumar Rahul and J.N. Jha 338
42. Effect of Relative Density on Interfacial Interaction between Granular
Soil and Glass Fiber
Akash Priyadarshee, Atul Kumar Rahul, Vikas Kumar,
Vijay Kumar and Ashish Kumar 347
43. Behaviour of Skirted Square Footing Resting on Sand Bed and
Backfilled by Pond Ash
Harwinder Kaur, Gurdeepak Singh and Amandeep Singh 354
Contents
[ x ]
44. Minimizing the Impact of Current Transients for Voltage Unbalance
on Induction Motor with SVPWM Inverter
Maninder Kaur, Gursewak Singh Brar and Ranvir Kaur 361
45. Soil Stabilization by using Coconut Shell Ash (CSAP) and Egg Shell
Powder (ESP)
Aakshi, Gurdeepak Singh and Pardeep Singh 369
46. Challenges and Opportunities in Big Data Platform Hadoop
Balraj Singh and Harsh Kumar Verma 379
47. Influence of Graphene Oxide (GPO) on the Properties of Cement
Composites
Sahibdeep Singh Setia, Sukhwinder Singh and Tanpreet Singh 387
AUTHOR INDEX 394
[ 96 ]
Selection of Inverter Technology Air Conditioner:
An Evaluation Based on Distance from
Average Solution
Rohit Dubey1, Raman Kumar2, Paramjit Singh Bilga3 and Sehijpal Singh4
1UG Student, Mechanical Engineering Department,
Guru Nanak Dev Engineering College, Ludhiana, Punjab, India
2,3,4Mechanical Engineering Department, Guru Nanak Dev Engineering College, Ludhiana, Punjab, India
Email: 1rohitdubey319@gmail.com, 2sehgal91@yahoo.co.in,
3psbilga@gndec.ac.in, 4sehijpalsingh@yahoo.in
ABSTRACT
The Evaluation Based on Distance from Average Solution (EDAS) technique based on the multi-criteria
decision-making method (MCDM) is utilized in the present work. Different air conditioners (AC) based on
inverter technology have been assessed as per user requirements with sustainable attributes. The AC is supplied
by various brands that have several features connected with it, like cost, power input, airflow, etc. The new
market accounts for a wide variety of consumer requirements. Therefore, offering a precise technique to pick
the best alternative for buyers/retailers/wholesalers to meet their obligations is the need of the hour. The data
of different ACs based on inverter technology were collected for decision making. The Equal weights method
is utilized to allocate weights of significance to the attributes. The ranks obtained with EDAS having identical
weights are displayed. The presented technique is handy for buyers/retailers/wholesalers. Further, the different
weighting techniques and more alternatives can be considered.
Keywords: Inverter Technology; Air Conditioners; Evaluation Based on Distance from Average Solution (EDAS);
MCDM; Energy Consumption
INTRODUCTION
Nowadays, air conditioning is generally recognized as necessary for comfortable living and working,
even though it was acknowledged as a luxury almost twenty years ago. It removes heat and humidity
from room to outside while cooling and pushing the fresh air inside (Adali and Isik, 2016). The AC
is now readily accessible with inverter technology. An inverter is a frequency converting device. The
technology is used in many home appliances, and it controls the voltage, current, and frequency
of the electricity. Inverter air-conditioners vary their cooling/heating efficiency by changing their
compressor’s power supply frequency. An inverter AC adjusts the compressor speed to regulate
the flow rate of the refrigerant and thus consumes less current and electricity. Inverter technology
has precise temperature regulation, and the device changes its capacity to avoid any variations in
temperature when the fixed temperature is reached. Non-inverter AC, by comparison, has a fixed
cooling/heating power and can regulate the indoor temperature only by starting or stopping their
compressors. Non-inverter AC stops and resumes again and again. The power consumption and
current decreases when the process ends, but it goes up sharply at restart time and therefore has
high average power consumption and fluctuations in temperature. So, inverter AC is more energy-
efficient than AC without inverter technology. The AC has many brands in the market and has
several associated features, such as cost, capacity, efficiency, power consumption, etc.
[ 97 ]
Selection of Inverter Technology Air Conditioner: An Evaluation Based on Distance from Average Solution
Hence the best selection of AC becomes difficult and turns out to be a problem of MCDM. Adali and
Işık (2016) selected an air conditioner with ARAS as well as with COPRAS techniques. Temucin and
Tozan (2016) decided air conditioners for a Turkish construction company with a fuzzy decision.
Vujicic et al. (2017) applied MOORA and SAW method to choose an air conditioner with objective
weights. Kumar et al. (2020a) picked a portable hard disk drive by using the WASPAS method.
Kumar et al. (2020b) also decided on the selection of vacuum cleaner with the TOPSIS technique.
The WSM and WPM techniques were utilized to pick a mobile phone (Kumar et al., 2020c). The
MCDM methods are applied successfully for selection purposes.
The EDAS method is a recently developed MCDM method by Keshavarz Ghorabaee (2015), which
is stable in different weights and well consistent with other techniques such as VIKOR, TOPSIS, SAW,
and COPRAS. EDAS is applied in various constructional and industrial applications. Ghorabaee et
al. (2016) used an extended EDAS method for supplier selection. Ghorabaee et al. (2017a) used
EDAS to solve real-life decision-making problems. Kahraman et al. (2017) proposed the intuitionistic
fuzzy EDAS method to evaluate solid waste disposal site alternatives. Stanujkic et al. (2017) used
the extended EDAS while using grey numbers and applied to solve a different numerical problem
and verified results with COPRAS and MOORA. Ghorabaee et al. (2017b) extended the EDAS for
type-2 fuzzy sets and applied to the subcontractor selection problem, and Ghorabaee et al. (2017c)
to order allocation with environmental contemplations and supplier selection. The algorithms for
neutrosophic soft decision making built based on EDAS (Peng and Liu, 2017). The Fuzzy AHP and
EDAS model are combined to select third-party logistics providers for the success of outsourcing
(Ecer, 2018). The SWARA method was used in conjunction with EDAS to evaluate the house plan
shape. The perfect carpenter manufacturer is also selected with fuzzy EDAS (Stevic et al., 2018). The
selection of appropriate subcontractors for the outsourcing of constructional projects was completed
with the EDAS (Ghorabaee et al., 2018). Zhang et al. (2019) united picture fuzzy motion and EDAS
to select a green supplier. Behzad et al. (2020) utilized BWM-EDAS to evaluate the concert of solid
waste management. The review of the literature indicates that EDAS has been successfully applied
in different fields of engineering and management. So, in this paper, it is utilized to select an air
conditioner based on inverter technology.
SELECTION OF AIR CONDITIONER BASED ON INVERTER TECHNOLOGY
The choice of AC is such that it must deliver appropriate air and more energy-efficient. The AC
comes in both inverter and non-inverter types. The primary difference is in the performance of
the compressor. The inverter type of AC is more energy-efficient and economical. In the present
study, the selection of inverter technology based AC is explored with the EDAS method. The data
for inverter AC was collected for application of room size 111-150 sq feet and of capacity 1.5 Ton.
Eleven brands are considered having nine attributes/criteria such as; Star Rating (C-1), Cooling
Capacity in Watt (C-2), Compressor Warranty in years (C-3), ISEER star rating (C-4), Air Flow in
CFM (C-5), Price of AC in INR (C-6), Noise Level in dB (C-7), Annual Energy Consumption in units
per year (C-8), and Power Input in Watts (C-9). The C-1, C-2, C-3, C-4, C-5 are beneficial, and
C-6, C-7, C-8, C-9 are non-beneficial criteria. In decision making, usually beneficial criteria refer to
a higher value, e.g., star rating of AC (maximization problem; means the higher value is desirable).
The non-beneficial rules apply to a lower value, e.g., cost of AC (minimization problem; means the
lower value is profitable). Subsequently, the EDAS strategy is used to pick the best solution from the
11 existing inverter air conditioner alternatives in the Indian market.
International Conference on Sustainable Development through Engineering Innovations
[ 98 ]
EVALUATION BASED UPON DISTANCE FROM AVERAGE SOLUTION;
EDAS METHOD
The detailed steps of the EDAS method for a decision-making problem can be carried out as per the
following procedure:
Step 1 Alternatives and attributes are worked out as per the objectives of the study.
Step 2 The Eq. (1) is presenting the decision pattern, and rows are assigned to one option (AC),
and all columns to one criterion (cost, power input, number of convenience features,
airflow, annual energy consumption, ISEER, etc. ). Accordingly, the eij of the decision
pattern ‘DP’ [eij; i = 1, 2, …, a no. of options (n), j = 1,2,..., no. of criteria (m)] are inputs:
(1)
Step 3 Determine the average solution (AVj) according to all the criteria as per the formula:
(2)
Step 4 The positive distance from the average (PDAij) is calculated according to the criteria which
are beneficial or non-beneficial:
(3a)
(3b)
Step 5 Calculate the negative distance from the average (NDAij) is calculated according to the
criteria:
(4a)
(4b)
Step 6 There are different techniques to allocate weights of importance to the responses. The
equal weights method is utilized in the present study; the total sum of all weights ought to
be unity:
Equal Weights Method
Equal weights are obtained by Eq. (5).
(5)
where m is a number of attributes.
Step 7 The Weighted sum of PDAij is obtained from the Average Matrix:
[ 99 ]
Selection of Inverter Technology Air Conditioner: An Evaluation Based on Distance from Average Solution
(6)
Step 8 The Weighted sum of NDAij is obtained from the Average Matrix:
(7)
Step 9 The Normalized values of SPi and SNi for all alternatives is calculated as follows:
(8a)
(8b)
where; NSPi and NSNi denote the normalized weighted sum of PDA and NDA, respectively.
Step 10 The appraisal score ASi for all alternatives is obtained as:
(9)
where; 0 ≤ ASi ≤1
Step 11 The alternatives are ranked according to the decreasing values of the appraisal score (ASi).
The option with the highest ASi is the best choice among the other options.
SELECTION OF INVERTER-TYPE AIR CONDITIONER: DECISION MAKING
WITH EDAS
The information obtained on the AC with the nine attributes such as C-1, C-2, C-3, C-4, C-5,
C-6, C-7, C-8, and C-9 of eleven brands from AC-1 to AC-11 is presented in Table 1. The C-1,
C-2, C-3, C-4, C-5 are beneficial, and C-6, C-7, C-8, C-9 are non-beneficial criteria. Table 1 is a
decision pattern as per Eq. (1). All the calculations are completed on Excel (MS-Office) up to four
decimal places.
Table 1: Decision Matrix for 11 Air Conditioners
Alternative C-1 C-2 C-3 C-4 C-5 C-6 C-7 C-8 C-9
AC-1 35187 10 3.86 568 43950 31 1027.92 1760
AC-2 55200 54.51 559.14 39990 43 892.32 1355
AC-3 55000 10 3.55 529.72 47000 42 834 1410
AC-4 35100 53.8 530 34999 36 1020.2 1730
AC-5 35000 53.7 572.1 38990 40 1045 1752
AC-6 35240 10 3.7 647.43 35490 43 1097.67 1660
AC-7 35200 10 3.8 490 36990 44 1058.16 1058
AC-8 35260 10 3.7 450 38990 35 1051.6 1663
AC-9 35100 53.58 500 35790 36 1123.74 1705
AC-10 35100 53.82 519 29999 32 1015.7 1580
AC-11 54800 43.62 625 59830 36 833 1430
Average 3.5455 5107.9091 7.1818 3.7855 544.5804 40183.4545 38 999.9376 1544.8182
International Conference on Sustainable Development through Engineering Innovations
[ 100 ]
Table 2: Positive Distance from Average
Alternative C-1 C-2 C-3 C-4 C-5 C-6 C-7 C-8 C-9
AC-1 0.0000 0.0155 0.3924 0.0197 0.0430 0.0000 0.1842 0.0000 0.0000
AC-2 0.4103 0.0180 0.0000 0.1914 0.0267 0.0048 0.0000 0.1076 0.1285
AC-3 0.4103 0.0000 0.3924 0.0000 0.0000 0.0000 0.0000 0.1659 0.0931
AC-4 0.0000 0.0000 0.0000 0.0038 0.0000 0.1290 0.0526 0.0000 0.0000
AC-5 0.0000 0.0000 0.0000 0.0000 0.0505 0.0297 0.0000 0.0000 0.0000
AC-6 0.0000 0.0259 0.3924 0.0000 0.1889 0.1168 0.0000 0.0000 0.0000
AC-7 0.0000 0.0180 0.3924 0.0038 0.0000 0.0795 0.0000 0.0000 0.3195
AC-8 0.0000 0.0298 0.3924 0.0000 0.0000 0.0297 0.0789 0.0000 0.0000
AC-9 0.0000 0.0000 0.0000 0.0000 0.0000 0.1093 0.0526 0.0000 0.0000
AC-10 0.0000 0.0000 0.0000 0.0091 0.0000 0.2534 0.1579 0.0000 0.0000
AC-11 0.4103 0.0000 0.0000 0.0000 0.1477 0.0000 0.0526 0.1669 0.0803
The Eq. (2) of step 3 is utilized to compute the average solution (AVj) of each criterion, and the
calculated values are shown in Table 1 in the last row. The Eq. (3a) is used to compute the positive
distance from average (PDAij) for beneficial criteria; in the present case, the beneficial criteria are
C-1, C-2, C-3, C-4, and C-5. The Eq. (3b) is utilized to calculate PDAij for non- beneficial criteria
such as C-6, C-7, C-8, and C-9. The computed information is displayed in Table 2 for PDAij as per
step 4. The Eq. (4a) is used to compute the negative distance from average (NDAij) for beneficial
criteria, e.g., the beneficial criteria are C-1, C-2, C-3, C-4, and C-5. The Eq. (4b) is utilized to
calculate NDAij for non- beneficial criteria such as C-6, C-7, C-8, and C-9. The calculated values are
presented in Table 3 for NDAij as per step 5.
Table 3: Negative Distance from Average
Alternative C-1 C-2 C-3 C-4 C-5 C-6 C-7 C-8 C-9
AC-1 0.1539 0.0000 0.0000 0.0000 0.0000 0.0937 0.0000 0.0280 0.1320
AC-2 0.0000 0.0000 0.3038 0.0000 0.0000 0.0000 0.1316 0.0000 0.0000
AC-3 0.0000 0.0211 0.0000 0.0622 0.0273 0.1696 0.1053 0.0000 0.0000
AC-4 0.1539 0.0015 0.3038 0.0000 0.0268 0.0000 0.0000 0.0203 0.1127
AC-5 0.1539 0.0211 0.3038 0.0226 0.0000 0.0000 0.0526 0.0451 0.1268
AC-6 0.1539 0.0000 0.0000 0.0226 0.0000 0.0000 0.1316 0.0977 0.0677
AC-7 0.1539 0.0000 0.0000 0.0000 0.1002 0.0000 0.1579 0.0582 0.0000
AC-8 0.1539 0.0000 0.0000 0.0226 0.1737 0.0000 0.0000 0.0517 0.0696
AC-9 0.1539 0.0015 0.3038 0.0543 0.0819 0.0000 0.0000 0.1238 0.0966
AC-10 0.1539 0.0015 0.3038 0.0000 0.0470 0.0000 0.0000 0.0158 0.0162
AC-11 0.0000 0.0603 0.4430 0.0437 0.0000 0.4889 0.0000 0.0000 0.0000
The Equal weights method is used to attain the weights of importance according to step 6 and Eq. (5),
e.g., there are nine attributes in the present case, and a weight for each response comes out to be 11.11%.
The Eq. (6) computes the weighted sum of PDAij according to step 7, and the computed information is
tabulated in Table 4.
[ 101 ]
Selection of Inverter Technology Air Conditioner: An Evaluation Based on Distance from Average Solution
Table 4: Weighted Sum of PDAij (SPi)
Alternative C-1 C-2 C-3 C-4 C-5 C-6 C-7 C-8 C-9 SPi
AC-1 0.0000 0.0017 0.0436 0.0022 0.0048 0.0000 0.0205 0.0000 0.0000 0.0728
AC-2 0.0456 0.0020 0.0000 0.0213 0.0030 0.0005 0.0000 0.0120 0.0143 0.0986
AC-3 0.0456 0.0000 0.0436 0.0000 0.0000 0.0000 0.0000 0.0184 0.0103 0.1180
AC-4 0.0000 0.0000 0.0000 0.0004 0.0000 0.0143 0.0058 0.0000 0.0000 0.0206
AC-5 0.0000 0.0000 0.0000 0.0000 0.0056 0.0033 0.0000 0.0000 0.0000 0.0089
AC-6 0.0000 0.0029 0.0436 0.0000 0.0210 0.0130 0.0000 0.0000 0.0000 0.0804
AC-7 0.0000 0.0020 0.0436 0.0004 0.0000 0.0088 0.0000 0.0000 0.0355 0.0904
AC-8 0.0000 0.0033 0.0436 0.0000 0.0000 0.0033 0.0088 0.0000 0.0000 0.0590
AC-9 0.0000 0.0000 0.0000 0.0000 0.0000 0.0121 0.0058 0.0000 0.0000 0.0180
AC-10 0.0000 0.0000 0.0000 0.0010 0.0000 0.0282 0.0175 0.0000 0.0000 0.0467
AC-11 0.0456 0.0000 0.0000 0.0000 0.0164 0.0000 0.0058 0.0185 0.0089 0.0953
Table 5: Weighted Sum of NDAij (SNi)
Alternative C-1 C-2 C-3 C-4 C-5 C-6 C-7 C-8 C-9 SNi
AC-1 0.0171 0.0000 0.0000 0.0000 0.0000 0.0104 0.0000 0.0031 0.0147 0.0453
AC-2 0.0000 0.0000 0.0338 0.0000 0.0000 0.0000 0.0146 0.0000 0.0000 0.0484
AC-3 0.0000 0.0023 0.0000 0.0069 0.0030 0.0188 0.0117 0.0000 0.0000 0.0428
AC-4 0.0171 0.0002 0.0338 0.0000 0.0030 0.0000 0.0000 0.0023 0.0125 0.0688
AC-5 0.0171 0.0023 0.0338 0.0025 0.0000 0.0000 0.0058 0.0050 0.0141 0.0807
AC-6 0.0171 0.0000 0.0000 0.0025 0.0000 0.0000 0.0146 0.0109 0.0075 0.0526
AC-7 0.0171 0.0000 0.0000 0.0000 0.0111 0.0000 0.0175 0.0065 0.0000 0.0522
AC-8 0.0171 0.0000 0.0000 0.0025 0.0193 0.0000 0.0000 0.0057 0.0077 0.0524
AC-9 0.0171 0.0002 0.0338 0.0060 0.0091 0.0000 0.0000 0.0138 0.0107 0.0906
AC-10 0.0171 0.0002 0.0338 0.0000 0.0052 0.0000 0.0000 0.0018 0.0018 0.0598
AC-11 0.0000 0.0067 0.0492 0.0049 0.0000 0.0543 0.0000 0.0000 0.0000 0.1151
The Eq. (7) computes the weighted sum of NDAij as per step 8, and the computed information is
tabulated in Table 5.
Table 6: Normalized Data, Appraisal Score and Ranks
Alternative SPi SNi NSPi NSNi ASi Rank
AC-1 0.0728 0.0453 0.6166 0.6066 0.6116 5
AC-2 0.0986 0.0484 0.8356 0.5797 0.7076 2
AC-3 0.1180 0.0428 0.9998 0.6278 0.8138 1
AC-4 0.0206 0.0688 0.1747 0.4025 0.2886 9
AC-5 0.0089 0.0807 0.0755 0.2993 0.1874 10
AC-6 0.0804 0.0526 0.6817 0.5430 0.6123 4
AC-7 0.0904 0.0522 0.7658 0.5461 0.6559 3
AC-8 0.0590 0.0524 0.4998 0.5450 0.5224 6
AC-9 0.0180 0.0906 0.1525 0.2125 0.1825 11
AC-10 0.0467 0.0598 0.3959 0.4805 0.4382 7
AC-11 0.0953 0.1151 0.8077 0.0000 0.4039 8
International Conference on Sustainable Development through Engineering Innovations
[ 102 ]
The normalized values of SPi are obtained with Eq. (8a) and SNi with Eq. (8b) for all alternatives are
presented in Table 6. The appraisal score ASi for all options is obtained by Eq. (9) and is shown in
Table 6. As per step 11, AC is ranked according to the decreasing values of the appraisal score. The
ranks are also displayed in Table 6. The AC-3 ranked one according to the highest appraisal score
of 0.8138, followed by AC-2 of ASi 0.7076 and AC-7 0.6559. The features associated with AC can
be seen from the decision matrix Table 1. The ranks attained with the EDAS method offer the user
or retailer or wholesaler to pick the best alternative accessible; if first is not convenient, and then
subsequently can be considered.
CONCLUSIONS
The present study aims to recognize the most suitable inverter technology air conditioner because it
is more energy-efficient than conventional AC, and the following conclusions are drawn.
Eleven AC alternatives were taken for decision making while considering nine attributes/
criteria. The recently developed EDAS technique is utilized to choose the best AC based
on the average solution. The weights of significance to the attributes/criteria are assigned
identically.
The findings indicate that the AC-3 is the first to be selected with the EDAS technique. It has
a price of 47000 in INR with features like airflow of 529.72 CFM, a noise level of 42 dB, the
annual energy consumption of 834 units per year, power input of 1410 Watt, cooling capacity
of 5000 Watt, star rating 5, ISEER rating of 3.55 and compressor warranty of 10 years.
If the first alternative is not available in the market, the next option may be chosen. The
EDAS approach has more statistical simplicity and the potential to produce more accurate
results.
Furthermore, the range of ACs can be expanded with more alternatives and attributes. The
weights of significance assignment can be considered with objective and subjective weights.
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... The results showed that Western Digital was the best brand out of the other four, as the top three models were from this brand in both weightage criteria [40]. The EDAS method was applied to select an inverter technology air conditioner from 11 different brands, and cost, power input, number of convenience features, airflow, annual energy consumption, and ISEER were the conflicting attributes [41]. WSM and WPM techniques were utilized to choose a mobile phone [42]. ...
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