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Multi-Step Wind Power Forecasting with Stacked Temporal Convolutional Network (S-TCN)

Author

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  • Huu Khoa Minh Nguyen

    (Faculty of Electrical and Electronics Engineering, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City 70000, Vietnam
    Vietnam National University Ho Chi Minh City (VNU-HCM), Linh Trung Ward, Thu Duc District, Ho Chi Minh City 70000, Vietnam)

  • Quoc-Dung Phan

    (Faculty of Electrical and Electronics Engineering, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City 70000, Vietnam
    Vietnam National University Ho Chi Minh City (VNU-HCM), Linh Trung Ward, Thu Duc District, Ho Chi Minh City 70000, Vietnam)

  • Yuan-Kang Wu

    (Department of Electrical Engineering, National Chung Cheng University, Chiayi 62102, Taiwan)

  • Quoc-Thang Phan

    (Department of Electrical Engineering, National Chung Cheng University, Chiayi 62102, Taiwan)

Abstract

Nowadays, wind power generation has become vital thanks to its advantages in cost, ecological friendliness, enormousness, and sustainability. However, the erratic and intermittent nature of this energy poses significant operational and management difficulties for power systems. Currently, the methods of wind power forecasting (WPF) are various and numerous. An accurate forecasting method of WPF can help system dispatchers plan unit commitment and reduce the risk of the unreliability of electricity supply. In order to improve the accuracy of short-term prediction for wind power and address the multi-step ahead forecasting, this research presents a Stacked Temporal Convolutional Network (S-TCN) model. By using dilated causal convolutions and residual connections, the suggested solution addresses the issue of long-term dependencies and performance degradation of deep convolutional models in sequence prediction. The simulation outcomes demonstrate that the S-TCN model’s training procedure is extremely stable and has a powerful capacity for generalization. Besides, the performance of the proposed model shows a higher forecasting accuracy compared to other existing neural networks like the Vanilla Long Short-Term Memory model or the Bidirectional Long Short-Term Memory model.

Suggested Citation

  • Huu Khoa Minh Nguyen & Quoc-Dung Phan & Yuan-Kang Wu & Quoc-Thang Phan, 2023. "Multi-Step Wind Power Forecasting with Stacked Temporal Convolutional Network (S-TCN)," Energies, MDPI, vol. 16(9), pages 1-20, April.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:9:p:3792-:d:1135745
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    References listed on IDEAS

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    1. Zhao, Weigang & Wei, Yi-Ming & Su, Zhongyue, 2016. "One day ahead wind speed forecasting: A resampling-based approach," Applied Energy, Elsevier, vol. 178(C), pages 886-901.
    2. Yusen Wang & Wenlong Liao & Yuqing Chang, 2018. "Gated Recurrent Unit Network-Based Short-Term Photovoltaic Forecasting," Energies, MDPI, vol. 11(8), pages 1-14, August.
    3. Duan, Jikai & Zuo, Hongchao & Bai, Yulong & Duan, Jizheng & Chang, Mingheng & Chen, Bolong, 2021. "Short-term wind speed forecasting using recurrent neural networks with error correction," Energy, Elsevier, vol. 217(C).
    4. Wang, Yun & Zou, Runmin & Liu, Fang & Zhang, Lingjun & Liu, Qianyi, 2021. "A review of wind speed and wind power forecasting with deep neural networks," Applied Energy, Elsevier, vol. 304(C).
    5. Ait Maatallah, Othman & Achuthan, Ajit & Janoyan, Kerop & Marzocca, Pier, 2015. "Recursive wind speed forecasting based on Hammerstein Auto-Regressive model," Applied Energy, Elsevier, vol. 145(C), pages 191-197.
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    2. Huiqun Yu & Haoyi Sun & Yueze Li & Chunmei Xu & Chenkun Du, 2024. "Enhanced Short-Term Load Forecasting: Error-Weighted and Hybrid Model Approach," Energies, MDPI, vol. 17(21), pages 1-22, October.

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