Short-term wind power forecasting integrating wake effect modeling with variational mode decomposition enhanced deep learning architectures
This work presents a robust hybrid framework for short-term wind power forecasting, validated on the GECAMA wind farm in Spain, which comprises 69 turbines and has a nominal capacity exceeding 300 MW. The proposed approach combines three established physics-based wake models (Jensen, Bastankhah, Lar...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2026 |
| País: | España |
| Institución: | Universidad de Alcalá (UAH) |
| Repositorio: | e_Buah Biblioteca Digital Universidad de Alcalá |
| Idioma: | inglés |
| OAI Identifier: | oai:ebuah.uah.es:10017/68278 |
| Acceso en línea: | http://hdl.handle.net/10017/68278 https://dx.doi.org/https://doi.org/10.1016/j.enconman.2025.120738 |
| Access Level: | acceso abierto |
| Palabra clave: | Wake models Wind farm layout Energy losses Wind direction uncertainty Turbulence Wind energy forecasting Energías Renovables/Energías Alternativas Alternative energies |
| Sumario: | This work presents a robust hybrid framework for short-term wind power forecasting, validated on the GECAMA wind farm in Spain, which comprises 69 turbines and has a nominal capacity exceeding 300 MW. The proposed approach combines three established physics-based wake models (Jensen, Bastankhah, Larsen) with five deep learning methods, further enhanced by input preprocessing via variational mode decomposition. Hourly energy production is forecasted using wind data from ECMWF, AROME, and ICON EU meteorological databases. Including wake models as input features helps reduce bias from meteorological signals by accounting for available wind turbines and physical effects, such as wake interactions and terrain. Adding previous forecast errors as features further boosts short-term accuracy. The hybrid models achieve error reductions of 40%-50% for one-hour-ahead forecasts, tapering to 1%-10% by 24 h. With variational mode decomposition (VMD), improvements reach 74%-77% for 3-6 h horizons and about 8% at 36 h. Across all horizons, VMD-enhanced models consistently outperform both standard hybrids and pure physical models. These results show that integrating wake modeling, deep learning, and advanced preprocessing is a practical way to improve wind power forecasts and support reliable electricity market decisions. |
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