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Day-Ahead Wind Power Forecasting Using a Two-Stage Hybrid Modeling Approach Based on SCADA and Meteorological Information, and Evaluating the Impact of Input-Data Dependency on Forecasting Accuracy

Author

Listed:
  • Dehua Zheng

    (Microgrid Platform R&D Center, Goldwind Science and Etechwin Electric Co., Ltd. BDA, Beijing 100176, China)

  • Min Shi

    (State Grid Hebei Electric Power Company, Shijiazhuang 050022, China)

  • Yifeng Wang

    (State Grid Hebei Electric Power Company, Shijiazhuang 050022, China)

  • Abinet Tesfaye Eseye

    (Microgrid Platform R&D Center, Goldwind Science and Etechwin Electric Co., Ltd. BDA, Beijing 100176, China
    School of Electrical and Electronic Engineering, North China Electric Power University, Changping District, Beijing 102206, China)

  • Jianhua Zhang

    (School of Electrical and Electronic Engineering, North China Electric Power University, Changping District, Beijing 102206, China)

Abstract

The power generated by wind generators is usually associated with uncertainties, due to the intermittency of wind speed and other weather variables. This creates a big challenge for transmission system operators (TSOs) and distribution system operators (DSOs) in terms of connecting, controlling and managing power networks with high-penetration wind energy. Hence, in these power networks, accurate wind power forecasts are essential for their reliable and efficient operation. They support TSOs and DSOs in enhancing the control and management of the power network. In this paper, a novel two-stage hybrid approach based on the combination of the Hilbert-Huang transform (HHT), genetic algorithm (GA) and artificial neural network (ANN) is proposed for day-ahead wind power forecasting. The approach is composed of two stages. The first stage utilizes numerical weather prediction (NWP) meteorological information to predict wind speed at the exact site of the wind farm. The second stage maps actual wind speed vs. power characteristics recorded by SCADA. Then, the wind speed forecast in the first stage for the future day is fed to the second stage to predict the future day’s wind power. Comparative selection of input-data parameter sets for the forecasting model and impact analysis of input-data dependency on forecasting accuracy have also been studied. The proposed approach achieves significant forecasting accuracy improvement compared with three other artificial intelligence-based forecasting approaches and a benchmark model using the smart persistence method.

Suggested Citation

  • Dehua Zheng & Min Shi & Yifeng Wang & Abinet Tesfaye Eseye & Jianhua Zhang, 2017. "Day-Ahead Wind Power Forecasting Using a Two-Stage Hybrid Modeling Approach Based on SCADA and Meteorological Information, and Evaluating the Impact of Input-Data Dependency on Forecasting Accuracy," Energies, MDPI, vol. 10(12), pages 1-23, December.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:12:p:1988-:d:121516
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    References listed on IDEAS

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    1. Catalão, J.P.S. & Pousinho, H.M.I. & Mendes, V.M.F., 2011. "Short-term wind power forecasting in Portugal by neural networks and wavelet transform," Renewable Energy, Elsevier, vol. 36(4), pages 1245-1251.
    2. Blonbou, Ruddy, 2011. "Very short-term wind power forecasting with neural networks and adaptive Bayesian learning," Renewable Energy, Elsevier, vol. 36(3), pages 1118-1124.
    3. Sanchez, Ismael, 2006. "Short-term prediction of wind energy production," International Journal of Forecasting, Elsevier, vol. 22(1), pages 43-56.
    4. Costa, Alexandre & Crespo, Antonio & Navarro, Jorge & Lizcano, Gil & Madsen, Henrik & Feitosa, Everaldo, 2008. "A review on the young history of the wind power short-term prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(6), pages 1725-1744, August.
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    4. Bogdan Bochenek & Jakub Jurasz & Adam Jaczewski & Gabriel Stachura & Piotr Sekuła & Tomasz Strzyżewski & Marcin Wdowikowski & Mariusz Figurski, 2021. "Day-Ahead Wind Power Forecasting in Poland Based on Numerical Weather Prediction," Energies, MDPI, vol. 14(8), pages 1-18, April.
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    6. Alexandru Pîrjan & George Căruțașu & Dana-Mihaela Petroșanu, 2018. "Designing, Developing, and Implementing a Forecasting Method for the Produced and Consumed Electricity in the Case of Small Wind Farms Situated on Quite Complex Hilly Terrain," Energies, MDPI, vol. 11(10), pages 1-42, October.
    7. Hugo T. V. Gouveia & Murilo A. Souza & Aida A. Ferreira & Jonata C. de Albuquerque & Otoni Nóbrega Neto & Milde Maria da Silva Lira & Ronaldo R. B. de Aquino, 2023. "Application of Augmented Echo State Networks and Genetic Algorithm to Improve Short-Term Wind Speed Forecasting," Energies, MDPI, vol. 16(6), pages 1-15, March.
    8. Robertas Lukočius & Žilvinas Nakutis & Vytautas Daunoras & Ramūnas Deltuva & Pranas Kuzas & Roma Račkienė, 2018. "An Analysis of the Systematic Error of a Remote Method for a Wattmeter Adjustment Gain Estimation in Smart Grids," Energies, MDPI, vol. 12(1), pages 1-26, December.
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