Integrated regime-aware wind power forecasting using multi-altitude meteorological features and hybrid machine learning
Yazarlar (2)
Dr. Öğr. Üyesi Murat IŞIK
Kırşehir Ahi Evran Üniversitesi, Türkiye
Dr. Öğr. Üyesi Mehmet Ali YALÇINKAYA
Kırşehir Ahi Evran Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | Frontiers in Energy Research (Q3) | ||
| Dergi ISSN | 2296-598X Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | Scopus | ||
| Makale Dili | İngilizce | Basım Tarihi | 01-2026 |
| Cilt / Sayı / Sayfa | 13 / 1 / – | DOI | 10.3389/fenrg.2025.1686125 |
| Makale Linki | https://doi.org/10.3389/fenrg.2025.1686125 | ||
| Özet |
Accurate wind power forecasting is critical for grid stability and renewable energy integration, yet the inherent variability of atmospheric conditions presents significant challenges. This study proposes a unified and scalable pipeline that integrates wind regime detection, temporal sequence modeling, and regime-conditioned deterministic and probabilistic power forecasting. Using 8 years of high-resolution meteorological data from multiple altitudes, we engineer a comprehensive set of physically interpretable features, including wind shear, temperature gradients, and rolling statistics. Regimes are identified via KMeans and Gaussian Mixture Models, with Principal Component Analysis applied post-clustering for visualization and interpretation. Temporal regime dynamics are characterized through both empirical and Markov transition matrices and modeled using Long Short-Term Memory (LSTM) networks for regime sequence prediction. For power forecasting, regime-specific models are developed using tuned ensemble regressors (XGBoost, LightGBM, CatBoost, and Random Forest), complemented by probabilistic approaches including Quantile Regression Forests, quantile-based XGBoost, and Bayesian neural networks. Results show that regime conditioning significantly enhances forecasting performance, with the stacked meta-learning ensemble achieving = 0.997 and over 30% reduction in MAE compared to baseline methods. Probabilistic models produce well-calibrated prediction intervals, providing uncertainty-aware forecasts suitable for operational decision-making. This work contributes a novel end-to-end framework that jointly models regime persistence, transitions, and regime-conditioned power output, incorporating uncertainty quantification through Quantile Regression Forests, quantile-based XGBoost, and Bayesian Neural Networks, thereby bridging a gap in the literature where these components are often treated in isolation. The approach advances both accuracy and interpretability, offering practical value for wind farm operation and renewable energy integration. |
| Anahtar Kelimeler |
| ensemble learning | hybrid machine learning | Markov chain modeling | probabilistic power forecasting | wind regime detection |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları |