Prioritization of thermal energy techniques by employing picture fuzzy soft power average and geometric aggregation operators
Abstract Energy storage is a way of storing energy to reduce imbalances between demand and energy production. The ability to store electricity and use it later is one of the keys to reaching large quantities of renewable energy on the grid. There are several methods to store energy such as mechanica...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Tahir Mahmood [verfasserIn] Jabbar Ahmmad [verfasserIn] Jeonghwan Gwak [verfasserIn] Naeem Jan [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Übergeordnetes Werk: |
In: Scientific Reports - Nature Portfolio, 2011, 13(2023), 1, Seite 26 |
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| Übergeordnetes Werk: |
volume:13 ; year:2023 ; number:1 ; pages:26 |
| Links: |
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| DOI / URN: |
10.1038/s41598-023-27387-9 |
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DOAJ080972462 |
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| 520 | |a Abstract Energy storage is a way of storing energy to reduce imbalances between demand and energy production. The ability to store electricity and use it later is one of the keys to reaching large quantities of renewable energy on the grid. There are several methods to store energy such as mechanical, electrical, chemical, electrochemical, and thermal energy. Regarding their operation, storage, and cost, the choice of these energy storage techniques appears to be interesting. This issue becomes very serious when there involves uncertainty. To consider this kind of uncertain information, a picture fuzzy soft set is found to be a more appropriate parameterization tool to deal with imprecise data. Based on the advanced structure of picture fuzzy soft set, here in this article, firstly, we have developed the notions of basic operational laws for picture fuzzy soft numbers. Then based on these developed operational laws, we have established the notions of picture fuzzy soft power average $$\left({\mathrm{PFS}}_{\mathrm{ft}}\mathrm{PA}\right)$$ PFS ft PA , weighted picture fuzzy soft power average $$\left({\mathrm{WPFS}}_{\mathrm{ft}}\mathrm{PA}\right)$$ WPFS ft PA and ordered weighted picture fuzzy soft power average $$\left({\mathrm{OWPFS}}_{\mathrm{ft}}\mathrm{PA}\right)$$ OWPFS ft PA aggregation operators. Moreover, we have introduced the notions for picture fuzzy soft power geometric $$\left({\mathrm{PFS}}_{\mathrm{ft}}\mathrm{PG}\right)$$ PFS ft PG , weighted picture fuzzy soft power geometric $$\left({\mathrm{WPFS}}_{\mathrm{ft}}\mathrm{PG}\right)$$ WPFS ft PG and ordered weighted picture fuzzy soft power geometric $$\left({\mathrm{OWPFS}}_{\mathrm{ft}}\mathrm{PG}\right)$$ OWPFS ft PG aggregation operators. Furthermore, we have established the application of picture fuzzy soft power aggregation operators for the selection of thermal energy storage techniques. For this, we have developed a decision-making approach along with an explanatory example to show the effective use of the developed theory. Furthermore, a comparative analysis of the introduced work shows the advancement of developed notions. | ||
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10.1038/s41598-023-27387-9 doi (DE-627)DOAJ080972462 (DE-599)DOAJe4e7e089788442818c3307ccd18400b0 DE-627 ger DE-627 rakwb eng Tahir Mahmood verfasserin aut Prioritization of thermal energy techniques by employing picture fuzzy soft power average and geometric aggregation operators 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Energy storage is a way of storing energy to reduce imbalances between demand and energy production. The ability to store electricity and use it later is one of the keys to reaching large quantities of renewable energy on the grid. There are several methods to store energy such as mechanical, electrical, chemical, electrochemical, and thermal energy. Regarding their operation, storage, and cost, the choice of these energy storage techniques appears to be interesting. This issue becomes very serious when there involves uncertainty. To consider this kind of uncertain information, a picture fuzzy soft set is found to be a more appropriate parameterization tool to deal with imprecise data. Based on the advanced structure of picture fuzzy soft set, here in this article, firstly, we have developed the notions of basic operational laws for picture fuzzy soft numbers. Then based on these developed operational laws, we have established the notions of picture fuzzy soft power average $$\left({\mathrm{PFS}}_{\mathrm{ft}}\mathrm{PA}\right)$$ PFS ft PA , weighted picture fuzzy soft power average $$\left({\mathrm{WPFS}}_{\mathrm{ft}}\mathrm{PA}\right)$$ WPFS ft PA and ordered weighted picture fuzzy soft power average $$\left({\mathrm{OWPFS}}_{\mathrm{ft}}\mathrm{PA}\right)$$ OWPFS ft PA aggregation operators. Moreover, we have introduced the notions for picture fuzzy soft power geometric $$\left({\mathrm{PFS}}_{\mathrm{ft}}\mathrm{PG}\right)$$ PFS ft PG , weighted picture fuzzy soft power geometric $$\left({\mathrm{WPFS}}_{\mathrm{ft}}\mathrm{PG}\right)$$ WPFS ft PG and ordered weighted picture fuzzy soft power geometric $$\left({\mathrm{OWPFS}}_{\mathrm{ft}}\mathrm{PG}\right)$$ OWPFS ft PG aggregation operators. Furthermore, we have established the application of picture fuzzy soft power aggregation operators for the selection of thermal energy storage techniques. For this, we have developed a decision-making approach along with an explanatory example to show the effective use of the developed theory. Furthermore, a comparative analysis of the introduced work shows the advancement of developed notions. Medicine R Science Q Jabbar Ahmmad verfasserin aut Jeonghwan Gwak verfasserin aut Naeem Jan verfasserin aut In Scientific Reports Nature Portfolio, 2011 13(2023), 1, Seite 26 (DE-627)663366712 (DE-600)2615211-3 20452322 nnns volume:13 year:2023 number:1 pages:26 https://doi.org/10.1038/s41598-023-27387-9 kostenfrei https://doaj.org/article/e4e7e089788442818c3307ccd18400b0 kostenfrei https://doi.org/10.1038/s41598-023-27387-9 kostenfrei https://doaj.org/toc/2045-2322 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 1 26 |
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10.1038/s41598-023-27387-9 doi (DE-627)DOAJ080972462 (DE-599)DOAJe4e7e089788442818c3307ccd18400b0 DE-627 ger DE-627 rakwb eng Tahir Mahmood verfasserin aut Prioritization of thermal energy techniques by employing picture fuzzy soft power average and geometric aggregation operators 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Energy storage is a way of storing energy to reduce imbalances between demand and energy production. The ability to store electricity and use it later is one of the keys to reaching large quantities of renewable energy on the grid. There are several methods to store energy such as mechanical, electrical, chemical, electrochemical, and thermal energy. Regarding their operation, storage, and cost, the choice of these energy storage techniques appears to be interesting. This issue becomes very serious when there involves uncertainty. To consider this kind of uncertain information, a picture fuzzy soft set is found to be a more appropriate parameterization tool to deal with imprecise data. Based on the advanced structure of picture fuzzy soft set, here in this article, firstly, we have developed the notions of basic operational laws for picture fuzzy soft numbers. Then based on these developed operational laws, we have established the notions of picture fuzzy soft power average $$\left({\mathrm{PFS}}_{\mathrm{ft}}\mathrm{PA}\right)$$ PFS ft PA , weighted picture fuzzy soft power average $$\left({\mathrm{WPFS}}_{\mathrm{ft}}\mathrm{PA}\right)$$ WPFS ft PA and ordered weighted picture fuzzy soft power average $$\left({\mathrm{OWPFS}}_{\mathrm{ft}}\mathrm{PA}\right)$$ OWPFS ft PA aggregation operators. Moreover, we have introduced the notions for picture fuzzy soft power geometric $$\left({\mathrm{PFS}}_{\mathrm{ft}}\mathrm{PG}\right)$$ PFS ft PG , weighted picture fuzzy soft power geometric $$\left({\mathrm{WPFS}}_{\mathrm{ft}}\mathrm{PG}\right)$$ WPFS ft PG and ordered weighted picture fuzzy soft power geometric $$\left({\mathrm{OWPFS}}_{\mathrm{ft}}\mathrm{PG}\right)$$ OWPFS ft PG aggregation operators. Furthermore, we have established the application of picture fuzzy soft power aggregation operators for the selection of thermal energy storage techniques. For this, we have developed a decision-making approach along with an explanatory example to show the effective use of the developed theory. Furthermore, a comparative analysis of the introduced work shows the advancement of developed notions. Medicine R Science Q Jabbar Ahmmad verfasserin aut Jeonghwan Gwak verfasserin aut Naeem Jan verfasserin aut In Scientific Reports Nature Portfolio, 2011 13(2023), 1, Seite 26 (DE-627)663366712 (DE-600)2615211-3 20452322 nnns volume:13 year:2023 number:1 pages:26 https://doi.org/10.1038/s41598-023-27387-9 kostenfrei https://doaj.org/article/e4e7e089788442818c3307ccd18400b0 kostenfrei https://doi.org/10.1038/s41598-023-27387-9 kostenfrei https://doaj.org/toc/2045-2322 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 1 26 |
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10.1038/s41598-023-27387-9 doi (DE-627)DOAJ080972462 (DE-599)DOAJe4e7e089788442818c3307ccd18400b0 DE-627 ger DE-627 rakwb eng Tahir Mahmood verfasserin aut Prioritization of thermal energy techniques by employing picture fuzzy soft power average and geometric aggregation operators 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Energy storage is a way of storing energy to reduce imbalances between demand and energy production. The ability to store electricity and use it later is one of the keys to reaching large quantities of renewable energy on the grid. There are several methods to store energy such as mechanical, electrical, chemical, electrochemical, and thermal energy. Regarding their operation, storage, and cost, the choice of these energy storage techniques appears to be interesting. This issue becomes very serious when there involves uncertainty. To consider this kind of uncertain information, a picture fuzzy soft set is found to be a more appropriate parameterization tool to deal with imprecise data. Based on the advanced structure of picture fuzzy soft set, here in this article, firstly, we have developed the notions of basic operational laws for picture fuzzy soft numbers. Then based on these developed operational laws, we have established the notions of picture fuzzy soft power average $$\left({\mathrm{PFS}}_{\mathrm{ft}}\mathrm{PA}\right)$$ PFS ft PA , weighted picture fuzzy soft power average $$\left({\mathrm{WPFS}}_{\mathrm{ft}}\mathrm{PA}\right)$$ WPFS ft PA and ordered weighted picture fuzzy soft power average $$\left({\mathrm{OWPFS}}_{\mathrm{ft}}\mathrm{PA}\right)$$ OWPFS ft PA aggregation operators. Moreover, we have introduced the notions for picture fuzzy soft power geometric $$\left({\mathrm{PFS}}_{\mathrm{ft}}\mathrm{PG}\right)$$ PFS ft PG , weighted picture fuzzy soft power geometric $$\left({\mathrm{WPFS}}_{\mathrm{ft}}\mathrm{PG}\right)$$ WPFS ft PG and ordered weighted picture fuzzy soft power geometric $$\left({\mathrm{OWPFS}}_{\mathrm{ft}}\mathrm{PG}\right)$$ OWPFS ft PG aggregation operators. Furthermore, we have established the application of picture fuzzy soft power aggregation operators for the selection of thermal energy storage techniques. For this, we have developed a decision-making approach along with an explanatory example to show the effective use of the developed theory. Furthermore, a comparative analysis of the introduced work shows the advancement of developed notions. Medicine R Science Q Jabbar Ahmmad verfasserin aut Jeonghwan Gwak verfasserin aut Naeem Jan verfasserin aut In Scientific Reports Nature Portfolio, 2011 13(2023), 1, Seite 26 (DE-627)663366712 (DE-600)2615211-3 20452322 nnns volume:13 year:2023 number:1 pages:26 https://doi.org/10.1038/s41598-023-27387-9 kostenfrei https://doaj.org/article/e4e7e089788442818c3307ccd18400b0 kostenfrei https://doi.org/10.1038/s41598-023-27387-9 kostenfrei https://doaj.org/toc/2045-2322 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 1 26 |
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10.1038/s41598-023-27387-9 doi (DE-627)DOAJ080972462 (DE-599)DOAJe4e7e089788442818c3307ccd18400b0 DE-627 ger DE-627 rakwb eng Tahir Mahmood verfasserin aut Prioritization of thermal energy techniques by employing picture fuzzy soft power average and geometric aggregation operators 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Energy storage is a way of storing energy to reduce imbalances between demand and energy production. The ability to store electricity and use it later is one of the keys to reaching large quantities of renewable energy on the grid. There are several methods to store energy such as mechanical, electrical, chemical, electrochemical, and thermal energy. Regarding their operation, storage, and cost, the choice of these energy storage techniques appears to be interesting. This issue becomes very serious when there involves uncertainty. To consider this kind of uncertain information, a picture fuzzy soft set is found to be a more appropriate parameterization tool to deal with imprecise data. Based on the advanced structure of picture fuzzy soft set, here in this article, firstly, we have developed the notions of basic operational laws for picture fuzzy soft numbers. Then based on these developed operational laws, we have established the notions of picture fuzzy soft power average $$\left({\mathrm{PFS}}_{\mathrm{ft}}\mathrm{PA}\right)$$ PFS ft PA , weighted picture fuzzy soft power average $$\left({\mathrm{WPFS}}_{\mathrm{ft}}\mathrm{PA}\right)$$ WPFS ft PA and ordered weighted picture fuzzy soft power average $$\left({\mathrm{OWPFS}}_{\mathrm{ft}}\mathrm{PA}\right)$$ OWPFS ft PA aggregation operators. Moreover, we have introduced the notions for picture fuzzy soft power geometric $$\left({\mathrm{PFS}}_{\mathrm{ft}}\mathrm{PG}\right)$$ PFS ft PG , weighted picture fuzzy soft power geometric $$\left({\mathrm{WPFS}}_{\mathrm{ft}}\mathrm{PG}\right)$$ WPFS ft PG and ordered weighted picture fuzzy soft power geometric $$\left({\mathrm{OWPFS}}_{\mathrm{ft}}\mathrm{PG}\right)$$ OWPFS ft PG aggregation operators. Furthermore, we have established the application of picture fuzzy soft power aggregation operators for the selection of thermal energy storage techniques. For this, we have developed a decision-making approach along with an explanatory example to show the effective use of the developed theory. Furthermore, a comparative analysis of the introduced work shows the advancement of developed notions. Medicine R Science Q Jabbar Ahmmad verfasserin aut Jeonghwan Gwak verfasserin aut Naeem Jan verfasserin aut In Scientific Reports Nature Portfolio, 2011 13(2023), 1, Seite 26 (DE-627)663366712 (DE-600)2615211-3 20452322 nnns volume:13 year:2023 number:1 pages:26 https://doi.org/10.1038/s41598-023-27387-9 kostenfrei https://doaj.org/article/e4e7e089788442818c3307ccd18400b0 kostenfrei https://doi.org/10.1038/s41598-023-27387-9 kostenfrei https://doaj.org/toc/2045-2322 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 1 26 |
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Abstract Energy storage is a way of storing energy to reduce imbalances between demand and energy production. The ability to store electricity and use it later is one of the keys to reaching large quantities of renewable energy on the grid. There are several methods to store energy such as mechanical, electrical, chemical, electrochemical, and thermal energy. Regarding their operation, storage, and cost, the choice of these energy storage techniques appears to be interesting. This issue becomes very serious when there involves uncertainty. To consider this kind of uncertain information, a picture fuzzy soft set is found to be a more appropriate parameterization tool to deal with imprecise data. Based on the advanced structure of picture fuzzy soft set, here in this article, firstly, we have developed the notions of basic operational laws for picture fuzzy soft numbers. Then based on these developed operational laws, we have established the notions of picture fuzzy soft power average $$\left({\mathrm{PFS}}_{\mathrm{ft}}\mathrm{PA}\right)$$ PFS ft PA , weighted picture fuzzy soft power average $$\left({\mathrm{WPFS}}_{\mathrm{ft}}\mathrm{PA}\right)$$ WPFS ft PA and ordered weighted picture fuzzy soft power average $$\left({\mathrm{OWPFS}}_{\mathrm{ft}}\mathrm{PA}\right)$$ OWPFS ft PA aggregation operators. Moreover, we have introduced the notions for picture fuzzy soft power geometric $$\left({\mathrm{PFS}}_{\mathrm{ft}}\mathrm{PG}\right)$$ PFS ft PG , weighted picture fuzzy soft power geometric $$\left({\mathrm{WPFS}}_{\mathrm{ft}}\mathrm{PG}\right)$$ WPFS ft PG and ordered weighted picture fuzzy soft power geometric $$\left({\mathrm{OWPFS}}_{\mathrm{ft}}\mathrm{PG}\right)$$ OWPFS ft PG aggregation operators. Furthermore, we have established the application of picture fuzzy soft power aggregation operators for the selection of thermal energy storage techniques. For this, we have developed a decision-making approach along with an explanatory example to show the effective use of the developed theory. Furthermore, a comparative analysis of the introduced work shows the advancement of developed notions. |
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Abstract Energy storage is a way of storing energy to reduce imbalances between demand and energy production. The ability to store electricity and use it later is one of the keys to reaching large quantities of renewable energy on the grid. There are several methods to store energy such as mechanical, electrical, chemical, electrochemical, and thermal energy. Regarding their operation, storage, and cost, the choice of these energy storage techniques appears to be interesting. This issue becomes very serious when there involves uncertainty. To consider this kind of uncertain information, a picture fuzzy soft set is found to be a more appropriate parameterization tool to deal with imprecise data. Based on the advanced structure of picture fuzzy soft set, here in this article, firstly, we have developed the notions of basic operational laws for picture fuzzy soft numbers. Then based on these developed operational laws, we have established the notions of picture fuzzy soft power average $$\left({\mathrm{PFS}}_{\mathrm{ft}}\mathrm{PA}\right)$$ PFS ft PA , weighted picture fuzzy soft power average $$\left({\mathrm{WPFS}}_{\mathrm{ft}}\mathrm{PA}\right)$$ WPFS ft PA and ordered weighted picture fuzzy soft power average $$\left({\mathrm{OWPFS}}_{\mathrm{ft}}\mathrm{PA}\right)$$ OWPFS ft PA aggregation operators. Moreover, we have introduced the notions for picture fuzzy soft power geometric $$\left({\mathrm{PFS}}_{\mathrm{ft}}\mathrm{PG}\right)$$ PFS ft PG , weighted picture fuzzy soft power geometric $$\left({\mathrm{WPFS}}_{\mathrm{ft}}\mathrm{PG}\right)$$ WPFS ft PG and ordered weighted picture fuzzy soft power geometric $$\left({\mathrm{OWPFS}}_{\mathrm{ft}}\mathrm{PG}\right)$$ OWPFS ft PG aggregation operators. Furthermore, we have established the application of picture fuzzy soft power aggregation operators for the selection of thermal energy storage techniques. For this, we have developed a decision-making approach along with an explanatory example to show the effective use of the developed theory. Furthermore, a comparative analysis of the introduced work shows the advancement of developed notions. |
| abstract_unstemmed |
Abstract Energy storage is a way of storing energy to reduce imbalances between demand and energy production. The ability to store electricity and use it later is one of the keys to reaching large quantities of renewable energy on the grid. There are several methods to store energy such as mechanical, electrical, chemical, electrochemical, and thermal energy. Regarding their operation, storage, and cost, the choice of these energy storage techniques appears to be interesting. This issue becomes very serious when there involves uncertainty. To consider this kind of uncertain information, a picture fuzzy soft set is found to be a more appropriate parameterization tool to deal with imprecise data. Based on the advanced structure of picture fuzzy soft set, here in this article, firstly, we have developed the notions of basic operational laws for picture fuzzy soft numbers. Then based on these developed operational laws, we have established the notions of picture fuzzy soft power average $$\left({\mathrm{PFS}}_{\mathrm{ft}}\mathrm{PA}\right)$$ PFS ft PA , weighted picture fuzzy soft power average $$\left({\mathrm{WPFS}}_{\mathrm{ft}}\mathrm{PA}\right)$$ WPFS ft PA and ordered weighted picture fuzzy soft power average $$\left({\mathrm{OWPFS}}_{\mathrm{ft}}\mathrm{PA}\right)$$ OWPFS ft PA aggregation operators. Moreover, we have introduced the notions for picture fuzzy soft power geometric $$\left({\mathrm{PFS}}_{\mathrm{ft}}\mathrm{PG}\right)$$ PFS ft PG , weighted picture fuzzy soft power geometric $$\left({\mathrm{WPFS}}_{\mathrm{ft}}\mathrm{PG}\right)$$ WPFS ft PG and ordered weighted picture fuzzy soft power geometric $$\left({\mathrm{OWPFS}}_{\mathrm{ft}}\mathrm{PG}\right)$$ OWPFS ft PG aggregation operators. Furthermore, we have established the application of picture fuzzy soft power aggregation operators for the selection of thermal energy storage techniques. For this, we have developed a decision-making approach along with an explanatory example to show the effective use of the developed theory. Furthermore, a comparative analysis of the introduced work shows the advancement of developed notions. |
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Prioritization of thermal energy techniques by employing picture fuzzy soft power average and geometric aggregation operators |
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https://doi.org/10.1038/s41598-023-27387-9 https://doaj.org/article/e4e7e089788442818c3307ccd18400b0 https://doaj.org/toc/2045-2322 |
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Jabbar Ahmmad Jeonghwan Gwak Naeem Jan |
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