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

Otero-Casal, Carlos; Patlakas, Platon; Prósper, Miguel A.; Galanis, George; Miguez-Macho, Gonzalo

doi:10.3390/en12163050

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

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Nonlinear Physics Group, Universidade de Santiago de Compostela, 15705 Santiago de Compostela, Spain

MeteoGalicia, Xunta de Galicia, 15707 Santiago de Compostela, Spain

School of Physics, Division of Environment and Meteorology, University of Athens, 15784 Athens, Greece

⁴

Siemens Gamesa, Meteorology Department, 28043 Madrid, Spain

⁵

Mathematical Modeling and Applications Laboratory, Hellenic Naval Academy, Hatzikiriakion, 18539 Piraeus, Greece

Author to whom correspondence should be addressed.

Energies 2019, 12(16), 3050; https://doi.org/10.3390/en12163050

Received: 2 June 2019 / Revised: 22 July 2019 / Accepted: 29 July 2019 / Published: 8 August 2019

(This article belongs to the Special Issue Renewable Energy Resource Assessment and Forecasting)

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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

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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. View Full-Text

Keywords: nowcasting; Kalman-Bayesian filter; WRF; high-resolution; complex terrain; wind

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MDPI and ACS Style

Otero-Casal, C.; Patlakas, P.; Prósper, M.A.; Galanis, G.; Miguez-Macho, G. Development of a High-Resolution Wind Forecast System Based on the WRF Model and a Hybrid Kalman-Bayesian Filter. Energies 2019, 12, 3050. https://doi.org/10.3390/en12163050

AMA Style

Otero-Casal C, Patlakas P, Prósper MA, Galanis G, Miguez-Macho G. Development of a High-Resolution Wind Forecast System Based on the WRF Model and a Hybrid Kalman-Bayesian Filter. Energies. 2019; 12(16):3050. https://doi.org/10.3390/en12163050

Chicago/Turabian Style

Otero-Casal, Carlos, Platon Patlakas, Miguel A. Prósper, George Galanis, and Gonzalo Miguez-Macho. 2019. "Development of a High-Resolution Wind Forecast System Based on the WRF Model and a Hybrid Kalman-Bayesian Filter" Energies 12, no. 16: 3050. https://doi.org/10.3390/en12163050

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

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