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Multi-criteria Decision Making for Solar Panel Selection Using Fuzzy Analytical Hierarchy Process and Technique for Order Preference by Similarity to ideal Solution (TOPSIS): An Empirical Study

  • Gnanasekaran SasikumarEmail author
  • Sivasangari Ayyappan
Case Study
  • 13 Downloads

Abstract

Solar energy has been one of the most utilized resources of non-conventional energy compared to wind and tidal energies. A photovoltaic system is used to generate solar energy by photovoltaics. The advancements emerged in solar power generation resulted as an economic alternative compared to conventional coal- or gas-based thermal power plants which are not only costly but pollute environment. The solar panel plays a vital role in a photovoltaic system, and a considerable research work is being done globally in order to reduce costs with better efficiency. Selection of solar panel encompasses complex factors involving both subjective and quantifiable parameters. Balancing both subjective and quantifiable parameters is essential for selecting appropriate solar panel. An integrated method is developed by combining Fuzzy analytical hierarchy process and Technique for Order Preference by Similarity to Ideal Solution, and the same is proposed for a 100 W solar panel selection. The case study was conducted to validate the proposed model for solar panel selection.

Keywords

Solar panel Analytical hierarchy process Fuzzy AHP TOPSIS 

List of symbols

\( C_{j} \)

A criterion j

\( D_{t} \)

Expert of selection t

\( \widetilde{\text{A}} \)

Fuzzy set

\( \widetilde{W} \)

Fuzzy weight vector

\( \widetilde{G}_{ij} \)

A value of alternative

\( C_{jk} \)

Sub-criterion

\( \widetilde{G}_{ijk} \)

A sub-score of alternative

\( \widetilde{w}_{i} \)

A weight for criterion i

Ai

An alternative i

H

Fuzzy judgement matrix

aij

Judgements

\( H_{\beta }^{\alpha } \)

Total crisp performance matrix

\( H^{\alpha } \)

Total interval performance matrix

DC

Pairwise comparison matrix

\( \beta \)

Risk index

\( C_{1} ,C_{2} , \ldots ,C_{n} \)

Set of decision elements

\( c_{j\beta }^{\alpha + } \) and \( c_{j\beta }^{\alpha - } \)

The best and worst crisp performance value among alternatives

\( h_{ij\beta }^{\alpha } \)

The crisp performance score

\( \alpha \)

Degree of confidence

\( M_{i\beta }^{\alpha + } \) and \( M_{i\beta }^{\alpha - } \)

Distance between ideal solutions and all the negative ideal solutions

\( I_{i\beta }^{\alpha } \)

Final performance score

\( \widetilde{h}_{ij} \)

Fuzzy performance score

\( \widetilde{a}_{ij} \)

Judgment score

\( \left( {L_{ij} ,M_{ij} ,U_{ij} } \right) \)

Fuzzy numbers

\( e_{lks} \)

Decision makers’ grades

Notes

Acknowledgements

The authors wish to thank their management for granting necessary permission to carry out the research work and reviewers for making significant contributions to enhance the article’s quality.

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Copyright information

© The Institution of Engineers (India) 2019

Authors and Affiliations

  1. 1.GMR Institute of TechnologySrikakulamIndia

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