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SP2LSTM: a patch learning-based electrical load forecasting for container terminal

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Abstract

Short-term electricity load forecasting plays a crucial role in modern container terminal. In this work, we design a short-term forecasting approach aimed at port load under the framework of patch learning. Firstly, singular spectrum analysis is applied to obtain denoised and noise features, respectively; then, a patch learning model based on the long short-term memory network is employed to address such a time-series forecasting problem. LSTM network and BiLSTM are considered as the global models to process denoised and noisy data, respectively, and convolutional neural network is selected as the patch model. Furthermore, an endpoint detection strategy is designed for adaptively identifying the positions of patches. The performance of the proposed model is tested and verified on a real Chinese container terminal load dataset. Experimental results show that the proposed approach, compared with state-of-the-art load forecasting models, has the greatest performance with respect to seven evaluation criteria.

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Data availability

The dataset generated during the current study is not publicly available due to data availability statement of support funding but is available from the corresponding author on reasonable request.

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Acknowledgements

This study is funded by the National Key Research and Development Program of China (No. 2021YFB2601604).

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Authors and Affiliations

  1. School of Transportation and Logistics, Wuhan University of Technology, Wuhan, China

    Jingjing Cao, Yujia Chen, Xiaohua Cao, Qiang Wang, Jiepeng Du & Zhipeng Wen

  2. School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China

    Bo Wang

  3. The Key Laboratory of Ministry of Education for Image Processing and Intelligent Control, Huazhong University of Science and Technology, Wuhan, China

    Bo Wang

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  1. Jingjing Cao
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  2. Yujia Chen
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  3. Xiaohua Cao
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  6. Jiepeng Du
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Correspondence to Xiaohua Cao.

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Cao, J., Chen, Y., Cao, X. et al. SP2LSTM: a patch learning-based electrical load forecasting for container terminal. Neural Comput & Applic 35, 22651–22669 (2023). https://doi.org/10.1007/s00521-023-08878-2

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  • DOI: https://doi.org/10.1007/s00521-023-08878-2

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