AUTHOR=Mushtaq Iram , Siddique Imran , Eldin Sayed M. , Majdoubi Jihen , Gurmani Shahid Hussain , Samar Mahvish , Zulqarnain Rana Muhammad 

TITLE=Prioritization of thermal energy storage techniques based on Einstein-ordered aggregation operators of q-rung orthopair fuzzy hypersoft sets

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 11 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2023.1119463

DOI=10.3389/fenrg.2023.1119463

ISSN=2296-598X

ABSTRACT=The capability to stock energy and consumption in the future is one of the keys to retrieving huge quantities of renewable energy on the grid. There are numerous techniques to stock energy, such as mechanical, electrical, chemical, electrochemical, and thermal. The q-rung orthopair fuzzy soft set (q-ROFSS) is a precise parametrization tool with fuzzy and uncertain contractions. In several environments, attributes are requisite to be further categorized because attribute values are not disjointed. The existing q-ROFSS configurations cannot resolve this state. Hypersoft sets is a leeway of soft sets (SS) that use multi-parameter approximation functions to overawed the inadequacies of prevailing SS structures. The significant impartiality of this investigation is to anticipate Einstein-ordered weighted aggregation operators (AOs) for q-rung orthopair fuzzy hypersoft sets (q-ROFHSS) such as q-rung orthopair fuzzy hypersoft Einstein-ordered weighted average (q-ROFHSEOWA) and q-rung orthopair fuzzy hypersoft Einstein-ordered weighted geometric (q-ROFHSEOWG) operators using the Einstein operational laws, with their requisite properties. Mathematical interpretations of decision-making (DM) constrictions deliberated to authorize the symmetry of the settled methodology. Einstein-ordered AOs, based on prospects, delivered a dynamic multi-criteria group decision (MCGDM) approach with the most significant consequences with the predominant MCGDM techniques. Also, we present the solicitation of Einstein-ordered weighted AOs for selecting thermal energy-storing technology. Moreover, a numerical example is described to determine the effective use of a decision-making pattern. The output of the suggested algorithm is more authentic than existing models and the most reliable to regulate the favourable features of the planned study.