Short-term load forecasting model based on gated recurrent unit and multi-head attention

doi:10.19682/j.cnki.1005-8885.2022.1019

中国邮电高校学报(英文) ›› 2023, Vol. 30 ›› Issue (3): 25-31.doi: 10.19682/j.cnki.1005-8885.2022.1019

• Artificial Intelligence • 上一篇下一篇

Short-term load forecasting model based on gated recurrent unit and multi-head attention

Li Hao, Zhang Linghua, Tong Cheng, Zhou Chenyang

1. School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China 2. School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China

收稿日期:2021-09-24 修回日期:2022-04-10 出版日期:2023-06-30 发布日期:2023-06-30
通讯作者: 张玲华 E-mail:zhanglh@njupt.edu.cn
基金资助:
This work was supported by the National Natural Science Foundation of China (61771258).

Short-term load forecasting model based on gated recurrent unit and multi-head attention

Li Hao, Zhang Linghua, Tong Cheng, Zhou Chenyang

1. School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China 2. School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China

Received:2021-09-24 Revised:2022-04-10 Online:2023-06-30 Published:2023-06-30
Contact: Zhang Linghua E-mail:zhanglh@njupt.edu.cn
Supported by:
This work was supported by the National Natural Science Foundation of China (61771258).

摘要/Abstract

摘要：

Short-term load forecasting (STLF) plays a crucial role in the smart grid. However, it is challenging to capture the long-time dependence and the nonlinear relationship due to the comprehensive fluctuations of the electrical load. In this paper, an STLF model based on gated recurrent unit and multi-head attention (GRU-MA) is proposed to address the aforementioned problems. The proposed model accommodates the time series and nonlinear relationship of load data through gated recurrent unit (GRU) and exploits multi-head attention (MA) to learn the decisive features and long-term dependencies. Additionally, the proposed model is compared with the supportvector regression (SVR) model, the recurrent neural network and multi-head attention (RNN-MA) model, the long short-term memory and multi-head attention (LSTM-MA ) model, the GRU model, and the temporal convolutional network (TCN) model using the public dataset of the Global Energy Forecasting Competition 2014 (GEFCOM2014). The results demonstrate that the GRU-MA model has the best prediction accuracy.

关键词:

deep learning, short-term load forecasting (STLF), gated recurrent unit (GRU), multi-head attention (MA)

Abstract: Short-term load forecasting (STLF) plays a crucial role in the smart grid. However, it is challenging to capture the long-time dependence and the nonlinear relationship due to the comprehensive fluctuations of the electrical load. In this paper, an STLF model based on gated recurrent unit and multi-head attention (GRU-MA) is proposed to address the aforementioned problems. The proposed model accommodates the time series and nonlinear relationship of load data through gated recurrent unit (GRU) and exploits multi-head attention (MA) to learn the decisive features and long-term dependencies. Additionally, the proposed model is compared with the supportvector regression (SVR) model, the recurrent neural network and multi-head attention (RNN-MA) model, the long short-term memory and multi-head attention (LSTM-MA ) model, the GRU model, and the temporal convolutional network (TCN) model using the public dataset of the Global Energy Forecasting Competition 2014 (GEFCOM2014). The results demonstrate that the GRU-MA model has the best prediction accuracy.

Key words: deep learning, short-term load forecasting (STLF), gated recurrent unit (GRU), multi-head attention (MA)

中图分类号:

TP183

Li Hao, Zhang Linghua, Tong Cheng, Zhou Chenyang. Short-term load forecasting model based on gated recurrent unit and multi-head attention[J]. The Journal of China Universities of Posts and Telecommunications, 2023, 30(3): 25-31.

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Short-term load forecasting model based on gated recurrent unit and multi-head attention

Short-term load forecasting model based on gated recurrent unit and multi-head attention

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