Real-time compatible wind power intensity forecasting for regional energy planning in complex inland regions
Wind Engineering, 2026 (ESCI, Scopus)
- Publication Type: Article / Article
- Publication Date: 2026
- Doi Number: 10.1177/0309524x261453187
- Journal Name: Wind Engineering
- Journal Indexes: Emerging Sources Citation Index (ESCI), Scopus, Compendex, Environment Index, Geobase, INSPEC
- Keywords: chronological data splitting, complex terrain, gradient boosting regression, real-time wind power prediction, wind power intensity
- Bursa Uludag University Affiliated: Yes
Abstract
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.