A HyF-WASPAS Method based on Similarity Measure to Access Renewable Energy Technologies

Abstract

Population growth and technological advancements have progressively expanded the demand in global energy. Growing public awareness about environmental problems, as well as the depletion of fossil resources, have prompted a worldwide shift to renewable energy sources. Renewable energy conveys an adaptable and sustainable approach for meeting future demands. Growing usage of renewable energy reinforces a significant combination of economic benefits, social progresses, environmental issues, technological advancements of sustainable solutions. Determining an efficient Renewable Energy Technology (RET) is a challenging task for decision-makers involving a variety of sustainability factors that creates uncertainty. The present research aims to offer a new, robust framework for evaluating RETs from a sustainable perspective by employing a novel similarity measure and the Weighted Aggregated Sum Product Assessment (WASPAS) approach in a Hyperbolic Fuzzy decision environment. Even though several kinds of studies have recently contributed to the evaluation of RET, no research has investigated RET in HyF framework, along with none of the existing literature has examined similarity measure on Hyperbolic Fuzzy Sets (HyFSs), which can effectively handle optimistic and pessimistic grades independently. Consequently, this paper firstly develops a novel similarity measure for HyFSs that efficaciously addresses all the axiomatic definitions and fundamental properties of a similarity measure on HyFSs. Moreover, its superiority and reliability are demonstrated through a comparative analysis with the existing similarity measures. Next, the WASPAS method is extended to solve Multiple-Criteria Decision Making (MCDM) problems in HyFSs. Further, we employ this proposed methodology towards the most effective RET selection while comparing this to the existing MCDM methods to validate its reliability and consistency. The experimental results indicate the proposed MCDM methodology successfully determines RETs in a Hyperbolic Fuzzy environment and exhibits higher consistency in comparison to the existing methods.