Decision Rules for Renewable Energy Utilization Using Rough Set Theory

Rough Set (RS) theory is used for data analysis and decision making where decision-making rules can be derived through attribute reduction and feature selection. Energy shortage is an issue for governments, and solar energy systems have become an important source of renewable energy. Rough sets may...
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Autor*in:	Chuying Huang [verfasserIn] Chun-Che Huang [verfasserIn] Din-Nan Chen [verfasserIn] Yuju Wang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Rough Set Theory decision making atribute reduction decision support sustainable feature selection

Übergeordnetes Werk:	In: Axioms - MDPI AG, 2012, 12(2023), 811, p 811
Übergeordnetes Werk:	volume:12 ; year:2023 ; number:811, p 811

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/axioms12090811

Katalog-ID:	DOAJ093456875

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spelling	10.3390/axioms12090811 doi (DE-627)DOAJ093456875 (DE-599)DOAJa9fdb2e320754d54ab9ce2dfe31d21b3 DE-627 ger DE-627 rakwb eng QA1-939 Chuying Huang verfasserin aut Decision Rules for Renewable Energy Utilization Using Rough Set Theory 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Rough Set (RS) theory is used for data analysis and decision making where decision-making rules can be derived through attribute reduction and feature selection. Energy shortage is an issue for governments, and solar energy systems have become an important source of renewable energy. Rough sets may be used to summarize and compare rule sets for different periods. In this study, the analysis of rules is an element of decision support that allows organizations to make better informed decisions. However, changes to decision rules require adjustment and analysis, and analysis is inhibited by changes in rules. With this consideration, a solution approach is proposed. The results show that not only can decision costs be reduced, but policymakers can also make it easier for the public to understand the incentives of green energy programs and the use of solar panels. The application process is simplified for the implementation of sustainable energy policies. Rough Set Theory decision making atribute reduction decision support sustainable feature selection Mathematics Chun-Che Huang verfasserin aut Din-Nan Chen verfasserin aut Yuju Wang verfasserin aut In Axioms MDPI AG, 2012 12(2023), 811, p 811 (DE-627)718622030 (DE-600)2661511-3 20751680 nnns volume:12 year:2023 number:811, p 811 https://doi.org/10.3390/axioms12090811 kostenfrei https://doaj.org/article/a9fdb2e320754d54ab9ce2dfe31d21b3 kostenfrei https://www.mdpi.com/2075-1680/12/9/811 kostenfrei https://doaj.org/toc/2075-1680 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2190 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2023 811, p 811
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allfieldsGer	10.3390/axioms12090811 doi (DE-627)DOAJ093456875 (DE-599)DOAJa9fdb2e320754d54ab9ce2dfe31d21b3 DE-627 ger DE-627 rakwb eng QA1-939 Chuying Huang verfasserin aut Decision Rules for Renewable Energy Utilization Using Rough Set Theory 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Rough Set (RS) theory is used for data analysis and decision making where decision-making rules can be derived through attribute reduction and feature selection. Energy shortage is an issue for governments, and solar energy systems have become an important source of renewable energy. Rough sets may be used to summarize and compare rule sets for different periods. In this study, the analysis of rules is an element of decision support that allows organizations to make better informed decisions. However, changes to decision rules require adjustment and analysis, and analysis is inhibited by changes in rules. With this consideration, a solution approach is proposed. The results show that not only can decision costs be reduced, but policymakers can also make it easier for the public to understand the incentives of green energy programs and the use of solar panels. The application process is simplified for the implementation of sustainable energy policies. Rough Set Theory decision making atribute reduction decision support sustainable feature selection Mathematics Chun-Che Huang verfasserin aut Din-Nan Chen verfasserin aut Yuju Wang verfasserin aut In Axioms MDPI AG, 2012 12(2023), 811, p 811 (DE-627)718622030 (DE-600)2661511-3 20751680 nnns volume:12 year:2023 number:811, p 811 https://doi.org/10.3390/axioms12090811 kostenfrei https://doaj.org/article/a9fdb2e320754d54ab9ce2dfe31d21b3 kostenfrei https://www.mdpi.com/2075-1680/12/9/811 kostenfrei https://doaj.org/toc/2075-1680 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2190 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2023 811, p 811
allfieldsSound	10.3390/axioms12090811 doi (DE-627)DOAJ093456875 (DE-599)DOAJa9fdb2e320754d54ab9ce2dfe31d21b3 DE-627 ger DE-627 rakwb eng QA1-939 Chuying Huang verfasserin aut Decision Rules for Renewable Energy Utilization Using Rough Set Theory 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Rough Set (RS) theory is used for data analysis and decision making where decision-making rules can be derived through attribute reduction and feature selection. Energy shortage is an issue for governments, and solar energy systems have become an important source of renewable energy. Rough sets may be used to summarize and compare rule sets for different periods. In this study, the analysis of rules is an element of decision support that allows organizations to make better informed decisions. However, changes to decision rules require adjustment and analysis, and analysis is inhibited by changes in rules. With this consideration, a solution approach is proposed. The results show that not only can decision costs be reduced, but policymakers can also make it easier for the public to understand the incentives of green energy programs and the use of solar panels. The application process is simplified for the implementation of sustainable energy policies. Rough Set Theory decision making atribute reduction decision support sustainable feature selection Mathematics Chun-Che Huang verfasserin aut Din-Nan Chen verfasserin aut Yuju Wang verfasserin aut In Axioms MDPI AG, 2012 12(2023), 811, p 811 (DE-627)718622030 (DE-600)2661511-3 20751680 nnns volume:12 year:2023 number:811, p 811 https://doi.org/10.3390/axioms12090811 kostenfrei https://doaj.org/article/a9fdb2e320754d54ab9ce2dfe31d21b3 kostenfrei https://www.mdpi.com/2075-1680/12/9/811 kostenfrei https://doaj.org/toc/2075-1680 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2190 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2023 811, p 811
language	English
source	In Axioms 12(2023), 811, p 811 volume:12 year:2023 number:811, p 811
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topic_facet	Rough Set Theory decision making atribute reduction decision support sustainable feature selection Mathematics
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authorswithroles_txt_mv	Chuying Huang @@aut@@ Chun-Che Huang @@aut@@ Din-Nan Chen @@aut@@ Yuju Wang @@aut@@
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callnumber-first	Q - Science
author	Chuying Huang
spellingShingle	Chuying Huang misc QA1-939 misc Rough Set Theory misc decision making misc atribute reduction misc decision support misc sustainable misc feature selection misc Mathematics Decision Rules for Renewable Energy Utilization Using Rough Set Theory
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topic_title	QA1-939 Decision Rules for Renewable Energy Utilization Using Rough Set Theory Rough Set Theory decision making atribute reduction decision support sustainable feature selection
topic	misc QA1-939 misc Rough Set Theory misc decision making misc atribute reduction misc decision support misc sustainable misc feature selection misc Mathematics
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title	Decision Rules for Renewable Energy Utilization Using Rough Set Theory
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title_full	Decision Rules for Renewable Energy Utilization Using Rough Set Theory
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title_sort	decision rules for renewable energy utilization using rough set theory
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title_auth	Decision Rules for Renewable Energy Utilization Using Rough Set Theory
abstract	Rough Set (RS) theory is used for data analysis and decision making where decision-making rules can be derived through attribute reduction and feature selection. Energy shortage is an issue for governments, and solar energy systems have become an important source of renewable energy. Rough sets may be used to summarize and compare rule sets for different periods. In this study, the analysis of rules is an element of decision support that allows organizations to make better informed decisions. However, changes to decision rules require adjustment and analysis, and analysis is inhibited by changes in rules. With this consideration, a solution approach is proposed. The results show that not only can decision costs be reduced, but policymakers can also make it easier for the public to understand the incentives of green energy programs and the use of solar panels. The application process is simplified for the implementation of sustainable energy policies.
abstractGer	Rough Set (RS) theory is used for data analysis and decision making where decision-making rules can be derived through attribute reduction and feature selection. Energy shortage is an issue for governments, and solar energy systems have become an important source of renewable energy. Rough sets may be used to summarize and compare rule sets for different periods. In this study, the analysis of rules is an element of decision support that allows organizations to make better informed decisions. However, changes to decision rules require adjustment and analysis, and analysis is inhibited by changes in rules. With this consideration, a solution approach is proposed. The results show that not only can decision costs be reduced, but policymakers can also make it easier for the public to understand the incentives of green energy programs and the use of solar panels. The application process is simplified for the implementation of sustainable energy policies.
abstract_unstemmed	Rough Set (RS) theory is used for data analysis and decision making where decision-making rules can be derived through attribute reduction and feature selection. Energy shortage is an issue for governments, and solar energy systems have become an important source of renewable energy. Rough sets may be used to summarize and compare rule sets for different periods. In this study, the analysis of rules is an element of decision support that allows organizations to make better informed decisions. However, changes to decision rules require adjustment and analysis, and analysis is inhibited by changes in rules. With this consideration, a solution approach is proposed. The results show that not only can decision costs be reduced, but policymakers can also make it easier for the public to understand the incentives of green energy programs and the use of solar panels. The application process is simplified for the implementation of sustainable energy policies.
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title_short	Decision Rules for Renewable Energy Utilization Using Rough Set Theory
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Decision Rules for Renewable Energy Utilization Using Rough Set Theory

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