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Hybrid Forecasting Model for Short-Term Wind Power Prediction Using Modified Long Short-Term Memory

Author

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  • Namrye Son

    (Department of Information and Communication Engineering, Honam University, Gwangsan-gu 62399, Korea)

  • Seunghak Yang

    (Department of Electrical Engineering, Honam University, Gwangsan-gu 62399, Korea)

  • Jeongseung Na

    (Department of Electrical Engineering, Honam University, Gwangsan-gu 62399, Korea)

Abstract

Renewable energy has recently gained considerable attention. In particular, the interest in wind energy is rapidly growing globally. However, the characteristics of instability and volatility in wind energy systems also affect power systems significantly. To address these issues, many studies have been carried out to predict wind speed and power. Methods of predicting wind energy are divided into four categories: physical methods, statistical methods, artificial intelligence methods, and hybrid methods. In this study, we proposed a hybrid model using modified LSTM (Long short-term Memory) to predict short-term wind power. The data adopted by modified LSTM use the current observation data (wind power, wind direction, and wind speed) rather than previous data, which are prediction factors of wind power. The performance of modified LSTM was compared among four multivariate models, which are derived from combining the current observation data. Among multivariable models, the proposed hybrid method showed good performance in the initial stage with Model 1 (wind power) and excellent performance in the middle to late stages with Model 3 (wind power, wind speed) in the estimation of short-term wind power. The experiment results showed that the proposed model is more robust and accurate in forecasting short-term wind power than the other models.

Suggested Citation

  • Namrye Son & Seunghak Yang & Jeongseung Na, 2019. "Hybrid Forecasting Model for Short-Term Wind Power Prediction Using Modified Long Short-Term Memory," Energies, MDPI, vol. 12(20), pages 1-17, October.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:20:p:3901-:d:276684
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    References listed on IDEAS

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    Cited by:

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    2. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    3. Xiangyue Wang & Ji Li & Lei Shao & Hongli Liu & Lei Ren & Lihua Zhu, 2023. "Short-Term Wind Power Prediction by an Extreme Learning Machine Based on an Improved Hunter–Prey Optimization Algorithm," Sustainability, MDPI, vol. 15(2), pages 1-14, January.
    4. Yiyang Sun & Xiangwen Wang & Junjie Yang, 2022. "Modified Particle Swarm Optimization with Attention-Based LSTM for Wind Power Prediction," Energies, MDPI, vol. 15(12), pages 1-17, June.
    5. Zhengxuan Xiao & Fei Tang & Mengyuan Wang, 2023. "Wind Power Short-Term Forecasting Method Based on LSTM and Multiple Error Correction," Sustainability, MDPI, vol. 15(4), pages 1-19, February.
    6. Mirosław Parol & Paweł Piotrowski & Piotr Kapler & Mariusz Piotrowski, 2021. "Forecasting of 10-Second Power Demand of Highly Variable Loads for Microgrid Operation Control," Energies, MDPI, vol. 14(5), pages 1-29, February.
    7. Muhammad Ahsan Zamee & Dongjun Won, 2020. "Novel Mode Adaptive Artificial Neural Network for Dynamic Learning: Application in Renewable Energy Sources Power Generation Prediction," Energies, MDPI, vol. 13(23), pages 1-29, December.
    8. Manisha Sawant & Rupali Patil & Tanmay Shikhare & Shreyas Nagle & Sakshi Chavan & Shivang Negi & Neeraj Dhanraj Bokde, 2022. "A Selective Review on Recent Advancements in Long, Short and Ultra-Short-Term Wind Power Prediction," Energies, MDPI, vol. 15(21), pages 1-24, October.
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