Multimodal Renewable Energy Hybrid Supply Optimization Model Based on Heterogeneous Cloud Wireless Access
With the increasing emphasis on environmental issues, the utilization of renewable energy has been recognized as a feasible solution to address the energy crisis and reduce environmental pollution. In view of this, this article proposes a multi-modal renewable energy hybrid power supply optimization...
Gespeichert in:
| Veröffentlicht in: | IEEE access 2024, Vol.12, p.78286-78303 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 78303 |
|---|---|
| container_issue | |
| container_start_page | 78286 |
| container_title | IEEE access |
| container_volume | 12 |
| creator | Tian, Feng Wang, Hongjiang Jiang, He Zhao, Baida |
| description | With the increasing emphasis on environmental issues, the utilization of renewable energy has been recognized as a feasible solution to address the energy crisis and reduce environmental pollution. In view of this, this article proposes a multi-modal renewable energy hybrid power supply optimization model based on heterogeneous cloud wireless access. The model innovatively combines heterogeneous cloud wireless access technology and various intelligent optimization algorithms, including k-clustering algorithm, particle swarm optimization algorithm, and whale optimization algorithm, forming a hybrid optimization algorithm. In order to comprehensively evaluate the actual performance of the model, this study recruited 20 experts to provide detailed ratings on four core dimensions: cost-benefit ratio, reliability, robustness, and user satisfaction. The results showed that the model scored 95.1, 96.4, 95.6, and 96.2 in the four dimensions of cost-benefit ratio, reliability indicators, robustness, and user satisfaction, respectively. This series of significant data not only confirms the theoretical superiority of the model, but also demonstrates its strong potential and practical value in practical applications. In summary, this study provides a promising and innovative solution for the field of renewable energy supply. |
| doi_str_mv | 10.1109/ACCESS.2024.3407726 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_3065464257</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>10542731</ieee_id><doaj_id>oai_doaj_org_article_b949e6374c634cf0a70369e567cc96a0</doaj_id><sourcerecordid>3065464257</sourcerecordid><originalsourceid>FETCH-LOGICAL-c289t-f099358eccb40b6bad9242b1f26cab49468660985e92e6e8e394f26cf5cd0b883</originalsourceid><addsrcrecordid>eNpNUV1LIzEUHRaFFfUX7D4EfG43k6-ZPNahWsEiWJd9DEnmTkmJzZjMIPXXb-oU8b7cr3POvXCK4leJ52WJ5Z9F0yw3mznBhM0pw1VFxI_igpRCziin4uxb_bO4TmmHc9R5xKuLwq1HP7jX0GqPnmEP79p4QMs9xO0BrQ4muhZtxr73B_TUZ6D70IMLe7QOLXh0qxO0KLcrGCCGbRYIY0KND2OL_rkIHlJCC2tzuirOO-0TXJ_yZfH3bvnSrGaPT_cPzeJxZkkth1mHpaS8BmsNw0YY3UrCiCk7Iqw2TDJRC4FlzUESEFADley467htsalrelk8TLpt0DvVR_eq40EF7dTnIMSt0nFw1oMykkkQtGJWUGY7rCtMhQQuKmul0Dhr3UxafQxvI6RB7cIY9_l9RbHgTDDCq4yiE8rGkFKE7utqidXRIjVZpI4WqZNFmfV7YjkA-MbgjFS0pP8BxUSNPw</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3065464257</pqid></control><display><type>article</type><title>Multimodal Renewable Energy Hybrid Supply Optimization Model Based on Heterogeneous Cloud Wireless Access</title><source>IEEE Open Access Journals</source><source>DOAJ Directory of Open Access Journals</source><source>EZB-FREE-00999 freely available EZB journals</source><creator>Tian, Feng ; Wang, Hongjiang ; Jiang, He ; Zhao, Baida</creator><creatorcontrib>Tian, Feng ; Wang, Hongjiang ; Jiang, He ; Zhao, Baida</creatorcontrib><description>With the increasing emphasis on environmental issues, the utilization of renewable energy has been recognized as a feasible solution to address the energy crisis and reduce environmental pollution. In view of this, this article proposes a multi-modal renewable energy hybrid power supply optimization model based on heterogeneous cloud wireless access. The model innovatively combines heterogeneous cloud wireless access technology and various intelligent optimization algorithms, including k-clustering algorithm, particle swarm optimization algorithm, and whale optimization algorithm, forming a hybrid optimization algorithm. In order to comprehensively evaluate the actual performance of the model, this study recruited 20 experts to provide detailed ratings on four core dimensions: cost-benefit ratio, reliability, robustness, and user satisfaction. The results showed that the model scored 95.1, 96.4, 95.6, and 96.2 in the four dimensions of cost-benefit ratio, reliability indicators, robustness, and user satisfaction, respectively. This series of significant data not only confirms the theoretical superiority of the model, but also demonstrates its strong potential and practical value in practical applications. In summary, this study provides a promising and innovative solution for the field of renewable energy supply.</description><identifier>ISSN: 2169-3536</identifier><identifier>EISSN: 2169-3536</identifier><identifier>DOI: 10.1109/ACCESS.2024.3407726</identifier><identifier>CODEN: IAECCG</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Algorithms ; Cloud computing ; Clustering ; Clustering algorithms ; Cost benefit analysis ; energy supply optimization ; heterogeneous cloud radio access ; Heuristic algorithms ; K-clustering algorithm ; Optimization ; Optimization algorithms ; Optimization models ; Particle swarm optimization ; particle swarm optimization algorithm ; Reliability ; Renewable energy ; Renewable energy sources ; Renewable resources ; Robustness (mathematics) ; User satisfaction ; Whale optimization algorithms ; Wireless communication ; WOA algorithm</subject><ispartof>IEEE access, 2024, Vol.12, p.78286-78303</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c289t-f099358eccb40b6bad9242b1f26cab49468660985e92e6e8e394f26cf5cd0b883</cites><orcidid>0009-0000-8303-5812</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10542731$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,864,2100,4021,27631,27921,27922,27923,54931</link.rule.ids></links><search><creatorcontrib>Tian, Feng</creatorcontrib><creatorcontrib>Wang, Hongjiang</creatorcontrib><creatorcontrib>Jiang, He</creatorcontrib><creatorcontrib>Zhao, Baida</creatorcontrib><title>Multimodal Renewable Energy Hybrid Supply Optimization Model Based on Heterogeneous Cloud Wireless Access</title><title>IEEE access</title><addtitle>Access</addtitle><description>With the increasing emphasis on environmental issues, the utilization of renewable energy has been recognized as a feasible solution to address the energy crisis and reduce environmental pollution. In view of this, this article proposes a multi-modal renewable energy hybrid power supply optimization model based on heterogeneous cloud wireless access. The model innovatively combines heterogeneous cloud wireless access technology and various intelligent optimization algorithms, including k-clustering algorithm, particle swarm optimization algorithm, and whale optimization algorithm, forming a hybrid optimization algorithm. In order to comprehensively evaluate the actual performance of the model, this study recruited 20 experts to provide detailed ratings on four core dimensions: cost-benefit ratio, reliability, robustness, and user satisfaction. The results showed that the model scored 95.1, 96.4, 95.6, and 96.2 in the four dimensions of cost-benefit ratio, reliability indicators, robustness, and user satisfaction, respectively. This series of significant data not only confirms the theoretical superiority of the model, but also demonstrates its strong potential and practical value in practical applications. In summary, this study provides a promising and innovative solution for the field of renewable energy supply.</description><subject>Algorithms</subject><subject>Cloud computing</subject><subject>Clustering</subject><subject>Clustering algorithms</subject><subject>Cost benefit analysis</subject><subject>energy supply optimization</subject><subject>heterogeneous cloud radio access</subject><subject>Heuristic algorithms</subject><subject>K-clustering algorithm</subject><subject>Optimization</subject><subject>Optimization algorithms</subject><subject>Optimization models</subject><subject>Particle swarm optimization</subject><subject>particle swarm optimization algorithm</subject><subject>Reliability</subject><subject>Renewable energy</subject><subject>Renewable energy sources</subject><subject>Renewable resources</subject><subject>Robustness (mathematics)</subject><subject>User satisfaction</subject><subject>Whale optimization algorithms</subject><subject>Wireless communication</subject><subject>WOA algorithm</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNUV1LIzEUHRaFFfUX7D4EfG43k6-ZPNahWsEiWJd9DEnmTkmJzZjMIPXXb-oU8b7cr3POvXCK4leJ52WJ5Z9F0yw3mznBhM0pw1VFxI_igpRCziin4uxb_bO4TmmHc9R5xKuLwq1HP7jX0GqPnmEP79p4QMs9xO0BrQ4muhZtxr73B_TUZ6D70IMLe7QOLXh0qxO0KLcrGCCGbRYIY0KND2OL_rkIHlJCC2tzuirOO-0TXJ_yZfH3bvnSrGaPT_cPzeJxZkkth1mHpaS8BmsNw0YY3UrCiCk7Iqw2TDJRC4FlzUESEFADley467htsalrelk8TLpt0DvVR_eq40EF7dTnIMSt0nFw1oMykkkQtGJWUGY7rCtMhQQuKmul0Dhr3UxafQxvI6RB7cIY9_l9RbHgTDDCq4yiE8rGkFKE7utqidXRIjVZpI4WqZNFmfV7YjkA-MbgjFS0pP8BxUSNPw</recordid><startdate>2024</startdate><enddate>2024</enddate><creator>Tian, Feng</creator><creator>Wang, Hongjiang</creator><creator>Jiang, He</creator><creator>Zhao, Baida</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>ESBDL</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7SR</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>DOA</scope><orcidid>https://orcid.org/0009-0000-8303-5812</orcidid></search><sort><creationdate>2024</creationdate><title>Multimodal Renewable Energy Hybrid Supply Optimization Model Based on Heterogeneous Cloud Wireless Access</title><author>Tian, Feng ; Wang, Hongjiang ; Jiang, He ; Zhao, Baida</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c289t-f099358eccb40b6bad9242b1f26cab49468660985e92e6e8e394f26cf5cd0b883</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Algorithms</topic><topic>Cloud computing</topic><topic>Clustering</topic><topic>Clustering algorithms</topic><topic>Cost benefit analysis</topic><topic>energy supply optimization</topic><topic>heterogeneous cloud radio access</topic><topic>Heuristic algorithms</topic><topic>K-clustering algorithm</topic><topic>Optimization</topic><topic>Optimization algorithms</topic><topic>Optimization models</topic><topic>Particle swarm optimization</topic><topic>particle swarm optimization algorithm</topic><topic>Reliability</topic><topic>Renewable energy</topic><topic>Renewable energy sources</topic><topic>Renewable resources</topic><topic>Robustness (mathematics)</topic><topic>User satisfaction</topic><topic>Whale optimization algorithms</topic><topic>Wireless communication</topic><topic>WOA algorithm</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Tian, Feng</creatorcontrib><creatorcontrib>Wang, Hongjiang</creatorcontrib><creatorcontrib>Jiang, He</creatorcontrib><creatorcontrib>Zhao, Baida</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005–Present</collection><collection>IEEE Open Access Journals</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>IEEE access</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Tian, Feng</au><au>Wang, Hongjiang</au><au>Jiang, He</au><au>Zhao, Baida</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Multimodal Renewable Energy Hybrid Supply Optimization Model Based on Heterogeneous Cloud Wireless Access</atitle><jtitle>IEEE access</jtitle><stitle>Access</stitle><date>2024</date><risdate>2024</risdate><volume>12</volume><spage>78286</spage><epage>78303</epage><pages>78286-78303</pages><issn>2169-3536</issn><eissn>2169-3536</eissn><coden>IAECCG</coden><abstract>With the increasing emphasis on environmental issues, the utilization of renewable energy has been recognized as a feasible solution to address the energy crisis and reduce environmental pollution. In view of this, this article proposes a multi-modal renewable energy hybrid power supply optimization model based on heterogeneous cloud wireless access. The model innovatively combines heterogeneous cloud wireless access technology and various intelligent optimization algorithms, including k-clustering algorithm, particle swarm optimization algorithm, and whale optimization algorithm, forming a hybrid optimization algorithm. In order to comprehensively evaluate the actual performance of the model, this study recruited 20 experts to provide detailed ratings on four core dimensions: cost-benefit ratio, reliability, robustness, and user satisfaction. The results showed that the model scored 95.1, 96.4, 95.6, and 96.2 in the four dimensions of cost-benefit ratio, reliability indicators, robustness, and user satisfaction, respectively. This series of significant data not only confirms the theoretical superiority of the model, but also demonstrates its strong potential and practical value in practical applications. In summary, this study provides a promising and innovative solution for the field of renewable energy supply.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/ACCESS.2024.3407726</doi><tpages>18</tpages><orcidid>https://orcid.org/0009-0000-8303-5812</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 2169-3536 |
| ispartof | IEEE access, 2024, Vol.12, p.78286-78303 |
| issn | 2169-3536 2169-3536 |
| language | eng |
| recordid | cdi_proquest_journals_3065464257 |
| source | IEEE Open Access Journals; DOAJ Directory of Open Access Journals; EZB-FREE-00999 freely available EZB journals |
| subjects | Algorithms Cloud computing Clustering Clustering algorithms Cost benefit analysis energy supply optimization heterogeneous cloud radio access Heuristic algorithms K-clustering algorithm Optimization Optimization algorithms Optimization models Particle swarm optimization particle swarm optimization algorithm Reliability Renewable energy Renewable energy sources Renewable resources Robustness (mathematics) User satisfaction Whale optimization algorithms Wireless communication WOA algorithm |
| title | Multimodal Renewable Energy Hybrid Supply Optimization Model Based on Heterogeneous Cloud Wireless Access |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-14T11%3A19%3A48IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Multimodal%20Renewable%20Energy%20Hybrid%20Supply%20Optimization%20Model%20Based%20on%20Heterogeneous%20Cloud%20Wireless%20Access&rft.jtitle=IEEE%20access&rft.au=Tian,%20Feng&rft.date=2024&rft.volume=12&rft.spage=78286&rft.epage=78303&rft.pages=78286-78303&rft.issn=2169-3536&rft.eissn=2169-3536&rft.coden=IAECCG&rft_id=info:doi/10.1109/ACCESS.2024.3407726&rft_dat=%3Cproquest_cross%3E3065464257%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3065464257&rft_id=info:pmid/&rft_ieee_id=10542731&rft_doaj_id=oai_doaj_org_article_b949e6374c634cf0a70369e567cc96a0&rfr_iscdi=true |