Data-Driven Prediction of Wave Energy Potential in the Black Sea Using Ensemble Learning and Real-Time Buoy Observations

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dc.contributor.authorIrim, Duygu Saydam
dc.contributor.authorSarikaya, Murat
dc.contributor.authorDagli, Salih
dc.date.accessioned2026-04-25T14:19:54Z
dc.date.available2026-04-25T14:19:54Z
dc.date.issued2026
dc.departmentSinop Üniversitesi
dc.description.abstractThis study proposes a machine learning-based framework for short- to medium-term (1-72 h) wave energy forecasting using buoy-measured wave parameters collected from Samsun and Ordu buoys in the Black Sea. Multiple machine learning (ML) techniques, including Long Short-Term Memory (LSTM), Extreme Learning Machine (ELM), Support Vector Machine (SVM), Bayesian Regression, Residual Neural Networks (ResNet), and Recurrent Neural Networks (RNN), were implemented to estimate wave power using forecasted wave parameters. Each model produced independent predictions, which were then integrated through a stacking ensemble approach based on Extreme Gradient Boosting (XGB) to obtain the final wave energy estimates. When compared individually, the ensemble framework provided noticeably lower error levels across all performance indicators, including RMSE, MAE, and MSE, and achieved higher coefficients of determination. Among the tested approaches, the XGB-based ensemble showed mostly reliable performance, particularly for short-term forecasts. For one-hour-ahead predictions, RMSE values were generally between 0.01 and 0.02 m, while R2 values were consistently above 0.99. Although prediction accuracy gradually decreased for longer horizons, the model remained stable for lead times of up to 72 h. Differences were also observed between buoy locations. Predictions derived from the Samsun buoy were more stable than those from Ordu, which is likely related to local variability in wave conditions. Our findings suggested that site-specific dynamics play an important role in wave energy forecasting. Overall, this work demonstrates the potential of combining real-time buoy measurements with ensemble machine learning to estimate wave energy in the Black Sea, providing a practical alternative to computationally intensive numerical wave models and supporting future offshore energy planning.
dc.description.sponsorshipSinop University
dc.description.sponsorshipOpen access funding provided by the Scientific and Technological Research Council of Turkiye (TUB & Idot;TAK).
dc.identifier.doi10.1007/s13369-026-11224-y
dc.identifier.issn2193-567X
dc.identifier.issn2191-4281
dc.identifier.scopus2-s2.0-105034409067
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1007/s13369-026-11224-y
dc.identifier.urihttps://hdl.handle.net/11486/8236
dc.identifier.wosWOS:001726487100001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer Heidelberg
dc.relation.ispartofArabian Journal for Science and Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260420
dc.subjectWave energy prediction
dc.subjectBlack sea
dc.subjectReal-time buoy data
dc.subjectEnsemble machine learning
dc.subjectEnergy forecasting
dc.subjectExtreme gradient boosting
dc.subjectRenewable energy integration
dc.titleData-Driven Prediction of Wave Energy Potential in the Black Sea Using Ensemble Learning and Real-Time Buoy Observations
dc.typeArticle

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