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Performance optimization of the parabolic trough power plant using a dual-stage ensemble algorithm

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dc.citedby1
dc.contributor.authorElfeky K.E.en_US
dc.contributor.authorHosny M.en_US
dc.contributor.authorMohammed A.G.en_US
dc.contributor.authorChu W.en_US
dc.contributor.authorKhatwa S.A.en_US
dc.contributor.authorWang Q.en_US
dc.contributor.authorid56979298200en_US
dc.contributor.authorid57192874374en_US
dc.contributor.authorid57219281767en_US
dc.contributor.authorid50261332200en_US
dc.contributor.authorid12141122800en_US
dc.contributor.authorid55521034600en_US
dc.date.accessioned2025-03-03T07:42:41Z
dc.date.available2025-03-03T07:42:41Z
dc.date.issued2024
dc.description.abstractThe flexibility of future power production systems must be maximized in order to offset the unpredictability of non-dispatchable energy from renewable sources. The absence of state policies and strategies to encourage investment in exploiting solar energy is why it is not widely used in Africa. In light of this, this research first aims to carry out a techno-economic assessment of parabolic trough power (PTP) plants in six cities in Egypt. For this purpose, a 100 MW nameplate capability has been simulated using the system advisor model simulation environment. Finally, four machine learning models are proposed, including artificial neural network, Gaussian process regression, regression neural network, and least square boosting in conjunction with a generalized additive model (GAM) as a meta-model, in order to develop a generalized model to predict the PTP performance based on the data of ten different cities at Africa. Utilizing these longstanding machine learning models for feature extraction, EnsGAM is tailored to the optimal predictors The findings indicate that, with a capacity factor of 55.4 % and an annual energy output of 484.7 GWh, the Benban location produces the most energy. In addition, Benban exhibits the shortest simple payback period?10.1 years?while Kuraymat displays the longest?11.5 years. The findings showed that EnsGAM performs noticeably better than all comparison techniques, producing the highest correlation coefficients/Willmott's agreement index for power generation and maximum discharge energy of 0.9463/0.9724 and 0.958/0.9778, respectively. ? 2024 Elsevier Ltden_US
dc.description.natureFinalen_US
dc.identifier.ArtNo123419
dc.identifier.doi10.1016/j.applthermaleng.2024.123419
dc.identifier.scopus2-s2.0-85193449446
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85193449446&doi=10.1016%2fj.applthermaleng.2024.123419&partnerID=40&md5=1f3b26cb99f5fa858c0e956ee089fe72
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36491
dc.identifier.volume249
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleApplied Thermal Engineering
dc.subjectAdaptive boosting
dc.subjectElectric load dispatching
dc.subjectInvestments
dc.subjectLearning systems
dc.subjectMachine learning
dc.subjectMeteorology
dc.subjectNeural networks
dc.subjectPower generation
dc.subjectSolar energy
dc.subjectEnsemble learning
dc.subjectLevelized cost of electricities
dc.subjectLevelized cost of electricity meteorological data
dc.subjectMachine learning models
dc.subjectMachine-learning
dc.subjectMeteorological data
dc.subjectParabolic trough
dc.subjectParabolic trough plant
dc.subjectParabolic trough power plants
dc.subjectPerformance optimizations
dc.subjectNameplates
dc.titlePerformance optimization of the parabolic trough power plant using a dual-stage ensemble algorithmen_US
dc.typeArticleen_US
dspace.entity.typePublication
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