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

Jian Zhu, Zhiyuan Zhao, Xiaoran Zheng, Zhao An, Qingwu Guo, Zhikai Li, Jianling Sun and Yuanjun Guo ()
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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
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

Energies, 2023, vol. 16, issue 22, 1-15

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.

Keywords: deep learning; power forecasting; wind and solar energy; transformer; filter (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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