Full Text:
<155>
CLC number:
On-line Access: 2023-02-24
Received: 2022-11-03
Revision Accepted: 2023-02-13
Crosschecked: 0000-00-00
Cited: 0
Clicked: 232
LDformer: a parallel neural network model for long-term power forecasting
| |||||||||||||
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, 1998, -1(-1): .
@article{title="LDformer: a parallel neural network model for long-term power forecasting",
author="Ran TIAN, Xinmei LI, Zhongyu MA, Yanxing LIU, Jingxia WANG Chu WANG",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2200540"
}
%0 Journal Article
%T LDformer: a parallel neural network model for long-term power forecasting
%A Ran TIAN
%A Xinmei LI
%A Zhongyu MA
%A Yanxing LIU
%A Jingxia WANG Chu WANG
%J Journal of Zhejiang University SCIENCE C
%V -1
%N -1
%P
%@ 2095-9184
%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2200540
TY - JOUR
T1 - LDformer: a parallel neural network model for long-term power forecasting
A1 - Ran TIAN
A1 - Xinmei LI
A1 - Zhongyu MA
A1 - Yanxing LIU
A1 - Jingxia WANG Chu WANG
J0 - Journal of Zhejiang University Science C
VL - -1
IS - -1
SP -
EP -
%@ 2095-9184
Y1 - 1998
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2200540
Abstract: Accurate long-time series power load forecasting is very important in the decision-making operation of the power grid and power consumption management of customers and can 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 long-term time series forecasting tasks with large amounts of data and high forecasting accuracy. To address this challenge, we propose a parallel time series forecasting model called LDformer. First, we combine the Informer with LSTM to obtain deep expression capability 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 ProbSparse self-attentive 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 baseline for most of the results in the different long time series prediction tasks.
Open peer comments: Debate/Discuss/Question/Opinion
<1>