Data-Driven Prediction of Wave Energy Potential in the Black Sea Using Ensemble Learning and Real-Time Buoy Observations
Yazarlar (3)
Duygu Saydam Irim
Sinop Üniversitesi, Türkiye
Sinop Üniversitesi, Türkiye
Prof. Dr. Murat SARIKAYA Sinop Üniversitesi, Türkiye
Doç. Dr. Salih DAĞLI Sinop Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | Arabian Journal for Science and Engineering (Q2) | ||
| Dergi ISSN | 2193-567X Dergi Bilgileri (2026) | ||
| Makale Dili | İngilizce | Basım Tarihi | 01-2026 |
| Kabul Tarihi | 02-03-2026 | Yayınlanma Tarihi | 27-03-2026 |
| Cilt / Sayı / Sayfa | 51 / 14 / 17069–17089 | DOI | 10.1007/s13369-026-11224-y |
| Makale Linki | https://link.springer.com/article/10.1007/s13369-026-11224-y | ||
| UAK Araştırma Alanları |
Mühendislik
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| Özet |
| This 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 … |
| Anahtar Kelimeler |
| Black sea | Energy forecasting | Ensemble machine learning | Extreme gradient boosting | Real-time buoy data | Renewable energy integration | Wave energy prediction |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Google Scholar | 1 |