Publication:
Prediction of hydropower generation via machine learning algorithms at three Gorges Dam, China

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dc.citedby20
dc.contributor.authorSattar Hanoon M.en_US
dc.contributor.authorNajah Ahmed A.en_US
dc.contributor.authorRazzaq A.en_US
dc.contributor.authorOudah A.Y.en_US
dc.contributor.authorAlkhayyat A.en_US
dc.contributor.authorFeng Huang Y.en_US
dc.contributor.authorkumar P.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57266877500en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid57219410567en_US
dc.contributor.authorid57210341575en_US
dc.contributor.authorid59268596900en_US
dc.contributor.authorid55807263900en_US
dc.contributor.authorid57206939156en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2024-10-14T03:18:37Z
dc.date.available2024-10-14T03:18:37Z
dc.date.issued2023
dc.description.abstractMachine learning models have been effectively applied to predict certain variable in several engineering applications where the variable is highly stochastic in nature and complex to identify utilizing the classical mathematical models. Therefore, this study investigates the capability of various machine learning algorithms in predicting the power production of a reservoir located in China using data from 1979 to 2016. In this study, different supervised and unsupervised machine learning algorithms are proposed: artificial neural network (ANN), AutoRegressive Integrated Moving Aveage (ARIMA) and support vector machine (SVM). Three different scenarios are examined, such as scenario1 (SC1): used to predict daily power generation, scenario 2 (SC2): used to predict power generation for monthly prediction and scenario 3 (SC3): used to predict hydropower generation (HPG) seasonally. The statistical analysis and pre-processing techniques were applied to the raw data before developing the models. Five statistical indexes are employed to evaluate the performances of various models developed. The results indicate that the proposed models can be used to predict HPG efficiently and could be an effective method for energy decision-makers. The sensitivity analyses found the most effective models for predicting HPG for three scenarios using graphical distribution data (Taylor diagram). Regarding the uncertainty analysis, 95PPU and d-factors were adopted to measure the uncertainties of the best models for ANN and SVM. The results presented that the value of 95PPU for all models falls into the range between 80% and 100%. As for the d-factor, all values in all scenarios are less than one. � 2022 THE AUTHORSen_US
dc.description.natureFinalen_US
dc.identifier.ArtNo101919
dc.identifier.doi10.1016/j.asej.2022.101919
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85136242754
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85136242754&doi=10.1016%2fj.asej.2022.101919&partnerID=40&md5=28d8bea2af06be79f2b62f64a311970d
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34245
dc.identifier.volume14
dc.publisherAin Shams Universityen_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGold Open Access
dc.sourceScopus
dc.sourcetitleAin Shams Engineering Journal
dc.subjectARIMA
dc.subjectartificial neural network (ANN)
dc.subjecthydropower generation (HPG)
dc.subjectmachine learning (ML)
dc.subjectsupport vector machine (SVM)
dc.subjectDecision making
dc.subjectForecasting
dc.subjectHydroelectric power
dc.subjectLearning algorithms
dc.subjectNeural networks
dc.subjectReservoirs (water)
dc.subjectSensitivity analysis
dc.subjectStochastic models
dc.subjectStochastic systems
dc.subjectUncertainty analysis
dc.subjectArtificial neural network
dc.subjectAuto-regressive
dc.subjectAutoregressive integrated moving aveage
dc.subjectHydro-power generation
dc.subjectHydropower generation
dc.subjectMachine learning
dc.subjectMachine learning algorithms
dc.subjectMachine-learning
dc.subjectSupport vector machine
dc.subjectSupport vectors machine
dc.subjectSupport vector machines
dc.titlePrediction of hydropower generation via machine learning algorithms at three Gorges Dam, Chinaen_US
dc.typeArticleen_US
dspace.entity.typePublication
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