Development of a High-Resolution Wind Forecast System Based on the WRF Model and a Hybrid Kalman-Bayesian Filter

Carlos Otero-Casal, Platon Patlakas, Miguel A. Prósper, George Galanis and Gonzalo Miguez-Macho
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Carlos Otero-Casal: Nonlinear Physics Group, Universidade de Santiago de Compostela, 15705 Santiago de Compostela, Spain
Platon Patlakas: School of Physics, Division of Environment and Meteorology, University of Athens, 15784 Athens, Greece
Miguel A. Prósper: Siemens Gamesa, Meteorology Department, 28043 Madrid, Spain
George Galanis: Mathematical Modeling and Applications Laboratory, Hellenic Naval Academy, Hatzikiriakion, 18539 Piraeus, Greece
Gonzalo Miguez-Macho: Nonlinear Physics Group, Universidade de Santiago de Compostela, 15705 Santiago de Compostela, Spain

Energies, 2019, vol. 12, issue 16, 1-19

Abstract: Regional microscale meteorological models have become a critical tool for wind farm production forecasting due to their capacity for resolving local flow dynamics. The high demand for reliable forecasting tools in the energy industry is the motivation for the development of an integrated system that combines the Weather Research and Forecasting (WRF) atmospheric model with an optimization obtained by the conjunction of a Kalman filter and a Bayesian model. This study focuses on the development and validation of this combined system in a very dense wind farm cluster located in Galicia (Northwest of Spain). A period of one year is simulated at 333 m horizontal resolution, with a daily operational forecasting set-up. The Kalman-Bayesian filter was tested both directly on wind speed and on the U-V (zonal and meridional) components for nowcasting periods from 10 min to 6 h periods, all of them with important applications in the wind industry. The results are quite promising, as the main statistical error indices are significantly improved in a 6 h forecasting horizon and even more in shorter horizon cases. The Mean Annual Error (MAE) for 1 h nowcasting horizon is 1.03 m/s for wind speed and 12.16 ° for wind direction. Moreover, the successful utilization of the integrated system in test cases with different characteristics demonstrates the potential utility that this tool may have for a variety of applications in wind farm operations and energy markets.

Keywords: nowcasting; Kalman-Bayesian filter; WRF; high-resolution; complex terrain; wind (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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