Please use this identifier to cite or link to this item: https://hdl.handle.net/10321/5700
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dc.contributor.authorAdebiyi, Abayomi Aduragbaen_US
dc.contributor.authorMoloi, Katlehoen_US
dc.contributor.authorAbbey, Xanderen_US
dc.date.accessioned2024-12-10T14:03:07Z-
dc.date.available2024-12-10T14:03:07Z-
dc.date.issued2024-11-28-
dc.identifier.citationAdebiyi, A.A., Moloi, K. and Abbey, X. 2024. Renewable energy site assessment with multi-criteria decision-making. Presented at: 2024 IEEE PES/IAS PowerAfrica, 1-6. doi:10.1109/PowerAfrica61624.2024.10759388en_US
dc.identifier.urihttps://hdl.handle.net/10321/5700-
dc.description.abstractWith global demand for renewable energy increasing, the search for suitable locations for renewable power plants has intensified. This paper presents a comprehensive site suitability assessment for solar power plants and wind farms using the Analytical Hierarchy Process (AHP) in conjunction with Geographic Information Systems (GIS). The Analytic Hierarchy Process forms the basis for the analysis of criteria while considering their relative importance, making it suitable for multi-criteria decision-making scenarios. Graphical Information Systems provide spatial analysis capabilities that enable the integration of various geographic information layers and facilitate informed decision-making. By combining AHP with GIS, this study offers a systematic approach for decision-makers and stakeholders in renewable energy industries worldwide to identify optimal locations for renewable power plants. The methodology encompasses data collection from reputable sources, extracting useful information from these sources and using them within the AHP framework, all within various Python software libraries. Through the integration of the aforementioned methodologies, this research contributes to the advancement of renewable energy site suitability assessment methodologies and supports the transition towards a more sustainable energy future.en_US
dc.format.extent6 pen_US
dc.language.isoenen_US
dc.publisherIEEE Exploreen_US
dc.relation.ispartof2024 IEEE PES/IAS PowerAfricaen_US
dc.subjectRenewable energyen_US
dc.subjectSolar power plantsen_US
dc.subjectWind farmsen_US
dc.subjectSite suitability assessmenten_US
dc.subjectAnalytical Hierarchy Processen_US
dc.subjectGeographic Information Systemsen_US
dc.subjectMulti-criteria decision-makingen_US
dc.titleRenewable energy site assessment with multi-criteria decision-makingen_US
dc.typeConferenceen_US
dc.date.updated2024-12-02T14:27:21Z-
dc.identifier.doi10.1109/PowerAfrica61624.2024.10759388-
item.grantfulltextopen-
item.cerifentitytypePublications-
item.fulltextWith Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.languageiso639-1en-
item.openairetypeConference-
Appears in Collections:Research Publications (Engineering and Built Environment)
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