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A short-term forecasting of wind power outputs using the enhanced wavelet transform and arimax techniques

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  • Ahn, EunJi
  • Hur, Jin

Abstract

South Korea has announced a plan to increase the proportion of renewable energy generation to 20% and reduce traditional energy generation by 2030. Among renewable energy resources, wind power has the advantage of relatively low power generation costs. However, it is difficult to forecast, as the output varies significantly depending on changing wind conditions such as the temperature, wind speed, and wind direction. We believe that short-term wind energy forecasts are the most important part for coping with these fluctuations and minimizing scheduling errors, thereby making the grid more reliable and reducing market service costs. Accordingly, we proposed a practical short-term wind power output forecasting method using a novel ensemble model based on a wavelet transform and autoregressive integrated moving average with explanatory variable (ARIMAX) approach. To demonstrate that the model has a good forecasting performance, we applied historical wind speed and wind power output data obtained from Jeju Island's wind farm to the model, and compared them with forecasted values. The normalized mean absolute error (NMAE) was used as the error metric. The comparison results were described for three supervisory control and data acquisition points. The average NMAE was approximately 3%. In addition, an N-1 contingency analysis was conducted to check the voltage profiles and flow limits in the context of a real power system, to ensure that the power system operated stably even with the forecasted values. The system worked successfully with the forecasted values, and can be deployed as application software for energy management systems in South Korea.

Suggested Citation

  • Ahn, EunJi & Hur, Jin, 2023. "A short-term forecasting of wind power outputs using the enhanced wavelet transform and arimax techniques," Renewable Energy, Elsevier, vol. 212(C), pages 394-402.
    • Handle: RePEc:eee:renene:v:212:y:2023:i:c:p:394-402
    DOI: 10.1016/j.renene.2023.05.048
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    References listed on IDEAS

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    1. Hu, Shuai & Xiang, Yue & Huo, Da & Jawad, Shafqat & Liu, Junyong, 2021. "An improved deep belief network based hybrid forecasting method for wind power," Energy, Elsevier, vol. 224(C).
    2. Erdem, Ergin & Shi, Jing, 2011. "ARMA based approaches for forecasting the tuple of wind speed and direction," Applied Energy, Elsevier, vol. 88(4), pages 1405-1414, April.
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    Citations

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    Cited by:

    1. Zhao, Yongning & Liao, Haohan & Zhao, Yuan & Pan, Shiji, 2025. "Data-augmented trend-fluctuation representations by interpretable contrastive learning for wind power forecasting," Applied Energy, Elsevier, vol. 380(C).
    2. Yanan Xue & Jinliang Yin & Xinhao Hou, 2024. "Short-Term Wind Power Prediction Based on Multi-Feature Domain Learning," Energies, MDPI, vol. 17(13), pages 1-25, July.
    3. Xiaoshuang Huang & Yinbao Zhang & Jianzhong Liu & Xinjia Zhang & Sicong Liu, 2023. "A Short-Term Wind Power Forecasting Model Based on 3D Convolutional Neural Network–Gated Recurrent Unit," Sustainability, MDPI, vol. 15(19), pages 1-13, September.
    4. Zhu, Yingqin & Liu, Yue & Wang, Nan & Zhang, ZhaoZhao & Li, YuanQiang, 2025. "Real-time Error Compensation Transfer Learning with Echo State Networks for Enhanced Wind Power Prediction," Applied Energy, Elsevier, vol. 379(C).
    5. Zhang, Yagang & Kong, Xue & Wang, Jingchao & Wang, Hui & Cheng, Xiaodan, 2024. "Wind power forecasting system with data enhancement and algorithm improvement," Renewable and Sustainable Energy Reviews, Elsevier, vol. 196(C).
    6. Bashir, Tasarruf & Wang, Huifang & Tahir, Mustafa & Zhang, Yixiang, 2025. "Wind and solar power forecasting based on hybrid CNN-ABiLSTM, CNN-transformer-MLP models," Renewable Energy, Elsevier, vol. 239(C).
    7. Mirza, Adeel Feroz & Shu, Zhaokun & Usman, Muhammad & Mansoor, Majad & Ling, Qiang, 2024. "Quantile-transformed multi-attention residual framework (QT-MARF) for medium-term PV and wind power prediction," Renewable Energy, Elsevier, vol. 220(C).
    8. Hu, Yue & Liu, Hanjing & Wu, Senzhen & Zhao, Yuan & Wang, Zhijin & Liu, Xiufeng, 2024. "Temporal collaborative attention for wind power forecasting," Applied Energy, Elsevier, vol. 357(C).
    9. Tang, Yugui & Zhang, Shujing & Zhang, Zhen, 2024. "A privacy-preserving framework integrating federated learning and transfer learning for wind power forecasting," Energy, Elsevier, vol. 286(C).

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