Time-Aware Machine Learning for Biomass Power Output Estimation Using SCADA Data
Yazarlar (2)
Dr. Öğr. Üyesi Ezgi GÜNEY Sinop Üniversitesi, Türkiye
Öğr. Gör. Memnun DEMİR 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) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 02-2026 |
| Kabul Tarihi | 03-02-2026 | Yayınlanma Tarihi | 19-02-2026 |
| Cilt / Sayı / Sayfa | 0 / 1 / – | DOI | 10.1007/s13369-026-11143-y |
| Makale Linki | https://doi.org/10.1007/s13369-026-11143-y | ||
| UAK Araştırma Alanları |
Elektrik Tesisleri
Makine Öğrenmesi
Yenilenebilir Enerji Sistemleri
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| Özet |
| Accurate short-term estimation of electrical power output in biomass power plants remains challenging due to the nonlinear and dynamically coupled nature of thermochemical conversion processes, fuel heterogeneity, and pronounced thermal inertia. Conventional physics-based models, while effective for steady-state analysis, often fail to capture the high-frequency dynamics required for real-time monitoring and decision-support applications. This study proposes a data-driven framework for short-term power output estimation using high-resolution Supervisory Control and Data Acquisition (SCADA) data collected from an operational industrial biomass power plant. A large-scale SCADA dataset comprising several hundred thousand time-stamped records is used to model the relationship between seven key thermodynamic and operational variables and net electrical power output. Multi-layer perceptron (MLP), random forest (RF … |
| Anahtar Kelimeler |
| Biomass | Machine learning | SCADA | Short-term power forecasting | Temporal information leakage |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Scopus | 1 |
| Google Scholar | 1 |