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CLC number: TP183

On-line Access: 2023-06-21

Received: 2022-11-03

Revision Accepted: 2023-09-21

Crosschecked: 2023-02-13

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Citations:  Bibtex RefMan EndNote GB/T7714




Xinmei LI


Zhongyu MA


Yanxing LIU


Jingxia WANG




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Frontiers of Information Technology & Electronic Engineering  2023 Vol.24 No.9 P.1287-1301


LDformer: a parallel neural network model for long-term power forecasting

Author(s):  Ran TIAN, Xinmei LI, Zhongyu MA, Yanxing LIU, Jingxia WANG, Chu WANG

Affiliation(s):  College of Computer Science & Engineering, Northwest Normal University, Lanzhou 730070, China

Corresponding email(s):   tianran@nwnu.edu.cn, 2020211978@nwnu.edu.cn, mazybg@nwnu.edu.cn, lyanxing@nwnu.edu.cn, 2020222004@nwnu.edu.cn, 2020221992@nwnu.edu.cn

Key Words:  Long-term power forecasting, Long short-term memory (LSTM), UniDrop, Self-attention mechanism

Ran TIAN, Xinmei LI, Zhongyu MA, Yanxing LIU, Jingxia WANG, Chu WANG. LDformer: a parallel neural network model for long-term power forecasting[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(9): 1287-1301.

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A1 - Ran TIAN
A1 - Xinmei LI
A1 - Zhongyu MA
A1 - Yanxing LIU
A1 - Jingxia WANG
A1 - Chu WANG
J0 - Frontiers of Information Technology & Electronic Engineering
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2200540

Accurate long-term power forecasting is important in the decision-making operation of the power grid and power consumption management of customers to ensure the power system’s reliable power supply and the grid economy’s reliable operation. However, most time-series forecasting models do not perform well in dealing with long-time-series prediction tasks with a large amount of data. To address this challenge, we propose a parallel time-series prediction model called LDformer. First, we combine Informer with long short-term memory (LSTM) to obtain deep representation abilities in the time series. Then, we propose a parallel encoder module to improve the robustness of the model and combine convolutional layers with an attention mechanism to avoid value redundancy in the attention mechanism. Finally, we propose a probabilistic sparse (ProbSparse) self-attention mechanism combined with uniDrop to reduce the computational overhead and mitigate the risk of losing some key connections in the sequence. Experimental results on five datasets show that LDformer outperforms the state-of-the-art methods for most of the cases when handling the different long-time-series prediction tasks.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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