Selection of Apt Renewable Energy Source for Smart Cities using Generalized Orthopair Fuzzy Information

Abstract

Renewable energy (RE) is a popular and clean source of energy that could potentially reduce carbon footprint and promote sustainable development in smart cities. Developing countries, such as India, have invested time, money, and effort into the proper development of smart cities. As there are different RE alternatives and several criteria used for its selection, researchers have adopted multi-criteria decision-making methods for systematic selection. Previous studies on RE selection did not (i) handle uncertainty effectively; (ii) calculate experts' weights systematically, and (iii) consider interdependencies among experts during aggregation. Motivated by these lacunas, this paper develops a new decision framework. The framework utilizes generalized orthopair fuzzy information, which is flexible and provides rich scope for handling uncertainty. Additionally, a regret theory-based weight calculation method is proposed for systematic weight calculation. Finally, Score-based Muirhead mean is proposed for aggregation of preferences and ranking of REs. An actual case study in Tamil Nadu is presented to exemplify the usefulness of the framework. Comparison with extant models reveals the superiorities of the framework.