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

Author

Listed:
  • Carlos Otero-Casal

    (Nonlinear Physics Group, Universidade de Santiago de Compostela, 15705 Santiago de Compostela, Spain
    MeteoGalicia, Xunta de Galicia, 15707 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)

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.

Suggested Citation

  • Carlos Otero-Casal & Platon Patlakas & Miguel A. Prósper & George Galanis & Gonzalo Miguez-Macho, 2019. "Development of a High-Resolution Wind Forecast System Based on the WRF Model and a Hybrid Kalman-Bayesian Filter," Energies, MDPI, vol. 12(16), pages 1-19, August.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:16:p:3050-:d:255744
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    References listed on IDEAS

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    1. Simão, H.P. & Powell, W.B. & Archer, C.L. & Kempton, W., 2017. "The challenge of integrating offshore wind power in the U.S. electric grid. Part II: Simulation of electricity market operations," Renewable Energy, Elsevier, vol. 103(C), pages 418-431.
    2. Archer, C.L. & Simão, H.P. & Kempton, W. & Powell, W.B. & Dvorak, M.J., 2017. "The challenge of integrating offshore wind power in the U.S. electric grid. Part I: Wind forecast error," Renewable Energy, Elsevier, vol. 103(C), pages 346-360.
    3. Drew, Daniel R. & Cannon, Dirk J. & Barlow, Janet F. & Coker, Phil J. & Frame, Thomas H.A., 2017. "The importance of forecasting regional wind power ramping: A case study for the UK," Renewable Energy, Elsevier, vol. 114(PB), pages 1201-1208.
    4. Zhao, Jing & Guo, Yanling & Xiao, Xia & Wang, Jianzhou & Chi, Dezhong & Guo, Zhenhai, 2017. "Multi-step wind speed and power forecasts based on a WRF simulation and an optimized association method," Applied Energy, Elsevier, vol. 197(C), pages 183-202.
    5. 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.
    6. Santos, J.A. & Rochinha, C. & Liberato, M.L.R. & Reyers, M. & Pinto, J.G., 2015. "Projected changes in wind energy potentials over Iberia," Renewable Energy, Elsevier, vol. 75(C), pages 68-80.
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    Cited by:

    1. Adedipe, Tosin & Shafiee, Mahmood & Zio, Enrico, 2020. "Bayesian Network Modelling for the Wind Energy Industry: An Overview," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    2. Mauro Caprabianca & Maria Carmen Falvo & Lorenzo Papi & Lucrezia Promutico & Viviana Rossetti & Federico Quaglia, 2020. "Replacement Reserve for the Italian Power System and Electricity Market," Energies, MDPI, vol. 13(11), pages 1-19, June.
    3. Wu, Qiang & Zheng, Hongling & Guo, Xiaozhu & Liu, Guangqiang, 2022. "Promoting wind energy for sustainable development by precise wind speed prediction based on graph neural networks," Renewable Energy, Elsevier, vol. 199(C), pages 977-992.

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