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Evaluation of Weather Information for Short-Term Wind Power Forecasting with Various Types of Models

Author

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  • Ju-Yeol Ryu

    (Institute for Advanced Engineering, Yongin 17180, Republic of Korea)

  • Bora Lee

    (Institute of Health & Environment, Seoul National University, Seoul 08826, Republic of Korea)

  • Sungho Park

    (Institute for Advanced Engineering, Yongin 17180, Republic of Korea)

  • Seonghyeon Hwang

    (Institute for Advanced Engineering, Yongin 17180, Republic of Korea)

  • Hyemin Park

    (Institute for Advanced Engineering, Yongin 17180, Republic of Korea)

  • Changhyeong Lee

    (Institute for Advanced Engineering, Yongin 17180, Republic of Korea)

  • Dohyeon Kwon

    (Institute for Advanced Engineering, Yongin 17180, Republic of Korea)

Abstract

The rising share of renewable energy in the energy mix brings with it new challenges such as power curtailment and lack of reliable large-scale energy grid. The forecasting of wind power generation for provision of flexibility, defined as the ability to absorb and manage fluctuations in the demand and supply by storing energy at times of surplus and releasing it when needed, is important. In this study, short-term forecasting models of wind power generation were developed using the conventional time-series method and hybrid models using support vector regression (SVR) based on rolling origin recalibration. For the application of the methodology, the meteorological database from Korea Meteorological Administration and actual operating data of a wind power turbine (2.3 MW) from 1 January to 31 December 2015 were used. The results showed that the proposed SVR model has higher forecasting accuracy than the existing time-series methods. In addition, the conventional time-series model has high accuracy under proper curation of wind turbine operation data. Therefore, the analysis results reveal that data curation and weather information are as important as the model for wind power forecasting.

Suggested Citation

  • Ju-Yeol Ryu & Bora Lee & Sungho Park & Seonghyeon Hwang & Hyemin Park & Changhyeong Lee & Dohyeon Kwon, 2022. "Evaluation of Weather Information for Short-Term Wind Power Forecasting with Various Types of Models," Energies, MDPI, vol. 15(24), pages 1-14, December.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:24:p:9403-:d:1001157
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    References listed on IDEAS

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    Cited by:

    1. Karthick Kanagarathinam & S. K. Aruna & S. Ravivarman & Mejdl Safran & Sultan Alfarhood & Waleed Alrajhi, 2023. "Enhancing Sustainable Urban Energy Management through Short-Term Wind Power Forecasting Using LSTM Neural Network," Sustainability, MDPI, vol. 15(18), pages 1-18, September.
    2. Chao-Ming Huang & Shin-Ju Chen & Sung-Pei Yang & Hsin-Jen Chen, 2023. "One-Day-Ahead Hourly Wind Power Forecasting Using Optimized Ensemble Prediction Methods," Energies, MDPI, vol. 16(6), pages 1-22, March.

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