%0 Journal Article
%A LI Hao
%A TONG Cheng
%A ZHANG Ling-Hua
%T <span>Short-term load forecasting model based on gated recurrent unit and multi-head attention</span>
%D 2023
%R 10.19682/j.cnki.1005-8885.2022.1019
%J Journal of China Universities of Posts and Telecommunications
%P 25-31
%V 30
%N 3
%X <span>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.</span>
%U https://jcupt.bupt.edu.cn/EN/10.19682/j.cnki.1005-8885.2022.1019