Akademik Veri Yönetim Sistemi
Wind Engineering, 2026 (ESCI, Scopus)
Accurate estimation of wind power intensity (WPI) is critical for regional energy planning, particularly in geographically complex inland regions where conventional analytical models often fail. This study proposes a real-time compatible regression framework for continuous wind power intensity prediction using daily meteorological data from the Maden region of Turkey. A Gradient Boosting Regression Tree (GBRT) model is trained offline on chronologically ordered data and deployed in a forward-only sequential prediction scheme that prevents access to future observations. The dataset is divided into training (70%), validation (10%), and real-time test (20%) subsets to realistically emulate operational conditions. Results demonstrate strong predictive performance on the real-time test set, with low error levels and high agreement between predicted and observed WPI values. Time-series and scatter analyses confirm the model’s ability to track temporal variations in wind power intensity under realistic deployment constraints. The proposed framework offers a practical and deployable solution for real-time wind power intensity estimation in complex inland terrains.