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Wind power forecasting for a real onshore wind farm on complex terrain using WRF high resolution simulations

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  • Prósper, Miguel A.
  • Otero-Casal, Carlos
  • Fernández, Felipe Canoura
  • Miguez-Macho, Gonzalo

Abstract

Regional meteorological models are becoming a generalized tool for wind resource forecasting, due to their capacity to simulate local flow dynamics impacting wind farm production. This study focuses on the case of production forecast and validation for a real onshore wind farm using high horizontal and vertical resolution WRF (Weather Research and Forecasting) model simulations. The wind farm is located in Galicia, in the northwest of Spain, in a complex region with high wind resource. Utilizing the Fitch scheme, specific for wind farms, a period of one year is simulated with a daily operational forecasting set-up. Power and wind predictions are obtained and compared with real data at each wind turbine hub, provided by the management company. Results show that WRF yields good wind power operational predictions for this kind of wind farms, due to a good representation of the planetary boundary layer behaviour of the region and the good performance of the Fitch scheme under these conditions. The best mean annual error (MAE) obtained is 1.87 m/s for wind speed and 14.75% for wind power. By comparing experiments with and without Fitch scheme, we estimate wind resource losses in the area due to the wake disturbances. The mean annual wake or environmental footprint of the farm extends for several kilometres in the southwest-northeast direction of the prevailing winds, with resource losses of 0.5% even at 17 km from the turbines.

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  • Prósper, Miguel A. & Otero-Casal, Carlos & Fernández, Felipe Canoura & Miguez-Macho, Gonzalo, 2019. "Wind power forecasting for a real onshore wind farm on complex terrain using WRF high resolution simulations," Renewable Energy, Elsevier, vol. 135(C), pages 674-686.
    • Handle: RePEc:eee:renene:v:135:y:2019:i:c:p:674-686
    DOI: 10.1016/j.renene.2018.12.047
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    15. González-Alonso de Linaje, N. & Mattar, C. & Borvarán, D., 2019. "Quantifying the wind energy potential differences using different WRF initial conditions on Mediterranean coast of Chile," Energy, Elsevier, vol. 188(C).
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    17. Ahmad, Tanveer & Zhang, Dongdong, 2022. "A data-driven deep sequence-to-sequence long-short memory method along with a gated recurrent neural network for wind power forecasting," Energy, Elsevier, vol. 239(PB).
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    19. Li, Jiale & Song, Zihao & Wang, Xuefei & Wang, Yanru & Jia, Yaya, 2022. "A novel offshore wind farm typhoon wind speed prediction model based on PSO–Bi-LSTM improved by VMD," Energy, Elsevier, vol. 251(C).
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    21. Martyna Kubiak & Artur Bugała & Dorota Bugała & Wojciech Czekała, 2025. "Simulation Analysis of Onshore and Offshore Wind Farms’ Generation Potential for Polish Climatic Conditions," Energies, MDPI, vol. 18(15), pages 1-42, August.
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    23. Lu, Hongfang & Ma, Xin & Huang, Kun & Azimi, Mohammadamin, 2020. "Prediction of offshore wind farm power using a novel two-stage model combining kernel-based nonlinear extension of the Arps decline model with a multi-objective grey wolf optimizer," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
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