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Time-Series Power Forecasting for Wind and Solar Energy Based on the SL-Transformer

Author

Listed:
  • Jian Zhu

    (SPIC Integrated Smart Energy Technology Co., Ltd., Beijing 100080, China)

  • Zhiyuan Zhao

    (SPIC Integrated Smart Energy Technology Co., Ltd., Beijing 100080, China)

  • Xiaoran Zheng

    (SPIC Integrated Smart Energy Technology Co., Ltd., Beijing 100080, China)

  • Zhao An

    (Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
    Guangdong Institute of Carbon Neutrality (Shaoguan), Shaoguan 512000, China)

  • Qingwu Guo

    (SPIC Integrated Smart Energy Technology Co., Ltd., Beijing 100080, China)

  • Zhikai Li

    (SPIC Integrated Smart Energy Technology Co., Ltd., Beijing 100080, China)

  • Jianling Sun

    (SPIC Integrated Smart Energy Technology Co., Ltd., Beijing 100080, China)

  • Yuanjun Guo

    (Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China)

Abstract

As the urgency to adopt renewable energy sources escalates, so does the need for accurate forecasting of power output, particularly for wind and solar power. Existing models often struggle with noise and temporal intricacies, necessitating more robust solutions. In response, our study presents the SL-Transformer, a novel method rooted in the deep learning paradigm tailored for green energy power forecasting. To ensure a reliable basis for further analysis and modeling, free from noise and outliers, we employed the SG filter and LOF algorithm for data cleansing. Moreover, we incorporated a self-attention mechanism, enhancing the model’s ability to discern and dynamically fine-tune input data weights. When benchmarked against other premier deep learning models, the SL-Transformer distinctly outperforms them. Notably, it achieves a near-perfect R 2 value of 0.9989 and a significantly low SMAPE of 5.8507% in wind power predictions. For solar energy forecasting, the SL-Transformer has achieved a SMAPE of 4.2156%, signifying a commendable improvement of 15% over competing models. The experimental results demonstrate the efficacy of the SL-Transformer in wind and solar energy forecasting.

Suggested Citation

  • Jian Zhu & Zhiyuan Zhao & Xiaoran Zheng & Zhao An & Qingwu Guo & Zhikai Li & Jianling Sun & Yuanjun Guo, 2023. "Time-Series Power Forecasting for Wind and Solar Energy Based on the SL-Transformer," Energies, MDPI, vol. 16(22), pages 1-15, November.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:22:p:7610-:d:1281643
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    References listed on IDEAS

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    Cited by:

    1. Hanif, M.F. & Mi, J., 2024. "Harnessing AI for solar energy: Emergence of transformer models," Applied Energy, Elsevier, vol. 369(C).

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