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CSP plants cooling technology: Techno-economic analysis, parametric study, and stacking ensemble learning forecasting

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dc.citedby0
dc.contributor.authorElsayed Elfeky K.en_US
dc.contributor.authorHosny M.en_US
dc.contributor.authorAbu Khatwa S.en_US
dc.contributor.authorGambo Mohammed A.en_US
dc.contributor.authorWang Q.en_US
dc.contributor.authorid56979298200en_US
dc.contributor.authorid57192874374en_US
dc.contributor.authorid58215796600en_US
dc.contributor.authorid57424399700en_US
dc.contributor.authorid55521034600en_US
dc.date.accessioned2025-03-03T07:41:59Z
dc.date.available2025-03-03T07:41:59Z
dc.date.issued2024
dc.description.abstractThe growing solar industry and technological developments that increase the efficiency and affordability of solar plants are driven by the growing need for sustainable energy sources. The selection of the type of cooling tower technology significantly impacts the overall performance of concentrating solar power (CSP) plants because the cooling towers are essential elements for heat expulsion. The primary objective is to assess the influence of cooling tower technology on CSP plants from the perspective of techno-economic performance by implementing wet, dry, and hybrid cooling systems and optimizing the variables affecting solar tower power plants by conducting a parametric analysis. Moreover, a unique stacking ensemble model comprising a dual-layer structure is developed for solar tower power plant performance prediction. Following the findings, dry and wet cooling technologies came in second and third, respectively, with the hybrid cooling technique achieving the best performance outcomes. By incorporating wet-dry as well as hybrid cooling towers at the Benban location, the levelized cost of electricity for the solar tower was determined to be 13.99, 13.62, and 13.37 �/kWh. The results show that based on the parametric assessment; the capacity factor rose from 11.73 to 73.13% when the mirror reflectance changed from 0.6 to 0.95% and the reflective area to profile ratio from 0.5 to 0.9%. The proposed stacking ensemble demonstrated superior performance compared to standalone base models and existing techniques. ? 2024 Elsevier Ltden_US
dc.description.natureFinalen_US
dc.identifier.ArtNo102777
dc.identifier.doi10.1016/j.tsep.2024.102777
dc.identifier.scopus2-s2.0-85201586542
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85201586542&doi=10.1016%2fj.tsep.2024.102777&partnerID=40&md5=24b25e1031c5f14d0370af657f72c22b
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36338
dc.identifier.volume54
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleThermal Science and Engineering Progress
dc.subjectConcentrating solar
dc.subjectConcentrating solar power plant
dc.subjectConvolutional neural network
dc.subjectCooling technology
dc.subjectDry cooling
dc.subjectHybrid cooling
dc.subjectLevelized cost of electricities
dc.subjectPower
dc.subjectStackings
dc.subjectWet cooling
dc.subjectSolar power plants
dc.titleCSP plants cooling technology: Techno-economic analysis, parametric study, and stacking ensemble learning forecastingen_US
dc.typeArticleen_US
dspace.entity.typePublication
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