Prioritization of thermal energy storage techniques based on Einstein-ordered aggregation operators of q-rung orthopair fuzzy hypersoft sets
The capability to stock energy and manage 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...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-03-01
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| Series: | Frontiers in Energy Research |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2023.1119463/full |
| _version_ | 1797858953832431616 |
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| author | Iram Mushtaq Imran Siddique Sayed M. Eldin Jihen Majdoubi Shahid Hussain Gurmani Mahvish Samar Rana Muhammad Zulqarnain |
| author_facet | Iram Mushtaq Imran Siddique Sayed M. Eldin Jihen Majdoubi Shahid Hussain Gurmani Mahvish Samar Rana Muhammad Zulqarnain |
| author_sort | Iram Mushtaq |
| collection | DOAJ |
| description | The capability to stock energy and manage 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, the attributes need to be further categorized because the attribute values are not disjointed. The existing q-rung orthopair fuzzy soft set configurations cannot resolve this state. Hypersoft sets are a leeway of soft sets (SSs) that use multi-parameter approximation functions to overcome the inadequacies of prevailing SS structures. The significance of this investigation lies in anticipating Einstein-ordered weighted aggregation operators (AOs) for q-rung orthopair fuzzy hypersoft sets (q-ROFHSSs), such as the q-rung orthopair fuzzy hypersoft Einstein-ordered weighted average (q-ROFHSEOWA) and the 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 constrictions are considered able to ensure the symmetry of the utilized methodology. Einstein-ordered aggregation operators, based on prospects, enable a dynamic multi-criteria group decision-making (MCGDM) approach with the most significant consequences with the predominant multi-criteria group decision techniques. Furthermore, we present the solicitation of Einstein-ordered weighted aggregation operators 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 favorable features of the planned study. |
| first_indexed | 2024-04-09T21:21:43Z |
| format | Article |
| id | doaj.art-8ae2d98de08743b2a15011b42eb7c651 |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-04-09T21:21:43Z |
| publishDate | 2023-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-8ae2d98de08743b2a15011b42eb7c6512023-03-28T05:02:21ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2023-03-011110.3389/fenrg.2023.11194631119463Prioritization of thermal energy storage techniques based on Einstein-ordered aggregation operators of q-rung orthopair fuzzy hypersoft setsIram Mushtaq0Imran Siddique1Sayed M. Eldin2Jihen Majdoubi3Shahid Hussain Gurmani4Mahvish Samar5Rana Muhammad Zulqarnain6School of Statistics and Data Science, Nankai University, Tianjin, ChinaDepartment of Mathematics, University of Management and Technology, Lahore, PakistanCenter of Research, Faculty of Engineering, Future University in Egypt New Cairo, Cairo, EgyptDepartment of Computer Science, College of Science and Humanities at Alghat, Majmaah University, Al-Majmaah, Saudi ArabiaSchool of Mathematical Sciences, Zhejiang Normal University, Jinhua, Zhejiang, ChinaSchool of Mathematical Sciences, Zhejiang Normal University, Jinhua, Zhejiang, ChinaSchool of Mathematical Sciences, Zhejiang Normal University, Jinhua, Zhejiang, ChinaThe capability to stock energy and manage 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, the attributes need to be further categorized because the attribute values are not disjointed. The existing q-rung orthopair fuzzy soft set configurations cannot resolve this state. Hypersoft sets are a leeway of soft sets (SSs) that use multi-parameter approximation functions to overcome the inadequacies of prevailing SS structures. The significance of this investigation lies in anticipating Einstein-ordered weighted aggregation operators (AOs) for q-rung orthopair fuzzy hypersoft sets (q-ROFHSSs), such as the q-rung orthopair fuzzy hypersoft Einstein-ordered weighted average (q-ROFHSEOWA) and the 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 constrictions are considered able to ensure the symmetry of the utilized methodology. Einstein-ordered aggregation operators, based on prospects, enable a dynamic multi-criteria group decision-making (MCGDM) approach with the most significant consequences with the predominant multi-criteria group decision techniques. Furthermore, we present the solicitation of Einstein-ordered weighted aggregation operators 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 favorable features of the planned study.https://www.frontiersin.org/articles/10.3389/fenrg.2023.1119463/fullq-rung orthopair fuzzy hypersoft setq-rung orthopair fuzzy hypersoft Einstein-ordered weighted average operatorq-rung orthopair fuzzy hypersoft Einstein-ordered weighted geometric operatormulti-criteria group decision makingthermal energy storage techniques |
| spellingShingle | Iram Mushtaq Imran Siddique Sayed M. Eldin Jihen Majdoubi Shahid Hussain Gurmani Mahvish Samar Rana Muhammad Zulqarnain Prioritization of thermal energy storage techniques based on Einstein-ordered aggregation operators of q-rung orthopair fuzzy hypersoft sets Frontiers in Energy Research q-rung orthopair fuzzy hypersoft set q-rung orthopair fuzzy hypersoft Einstein-ordered weighted average operator q-rung orthopair fuzzy hypersoft Einstein-ordered weighted geometric operator multi-criteria group decision making thermal energy storage techniques |
| title | Prioritization of thermal energy storage techniques based on Einstein-ordered aggregation operators of q-rung orthopair fuzzy hypersoft sets |
| title_full | Prioritization of thermal energy storage techniques based on Einstein-ordered aggregation operators of q-rung orthopair fuzzy hypersoft sets |
| title_fullStr | Prioritization of thermal energy storage techniques based on Einstein-ordered aggregation operators of q-rung orthopair fuzzy hypersoft sets |
| title_full_unstemmed | Prioritization of thermal energy storage techniques based on Einstein-ordered aggregation operators of q-rung orthopair fuzzy hypersoft sets |
| title_short | Prioritization of thermal energy storage techniques based on Einstein-ordered aggregation operators of q-rung orthopair fuzzy hypersoft sets |
| title_sort | prioritization of thermal energy storage techniques based on einstein ordered aggregation operators of q rung orthopair fuzzy hypersoft sets |
| topic | q-rung orthopair fuzzy hypersoft set q-rung orthopair fuzzy hypersoft Einstein-ordered weighted average operator q-rung orthopair fuzzy hypersoft Einstein-ordered weighted geometric operator multi-criteria group decision making thermal energy storage techniques |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2023.1119463/full |
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