scholarly journals Attention Based Spatial-Temporal Graph Convolutional Networks for Short-term Load Forecasting

2021 ◽  
Vol 2078 (1) ◽  
pp. 012051
Author(s):  
Rong Liu ◽  
Luan Chen
Keyword(s):  
Power System ◽  
Network Structure ◽  
Power Grid ◽  
Load Forecasting ◽  
Experimental Results ◽  
Short Term ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Short Term Load Forecasting ◽  
Temporal Graph

Abstract To predict the load of the power system with a known network structure, this paper proposes a novel attention based spatial-temporal graph convolutional network (ASTGCN) model to predict the node load in the power grid. The experimental results show the good performance of ASTGCN.

A Method Based on Wavelet Neural Network for Power System Short Term Load Forecasting

2014 ◽  
Vol 521 ◽  
pp. 303-306 ◽  
Author(s):  
Hong Mei Zhong ◽  
Jie Liu ◽  
Qi Fang Chen ◽  
Nian Liu
Keyword(s):  
Neural Network ◽  
Power System ◽  
Bp Neural Network ◽  
Load Forecasting ◽  
Wavelet Neural Network ◽  
Signal Spectrum ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Load Sequence ◽  
Daily Load

The short-term load of Power System is uncertain and the daily-load signal spectrum is continuous. The approach of Wavelet Neural Network (WNN) is proposed by combing the wavelet transform (WT) and neural network. By the WT, the time-based short-term load sequence can be decomposed into different scales sequences, which is used to training the BP neural network. The short-term load is forecasted by the trained BP neural network. Select the load of a random day in Lianyungang to study, according to the numerical simulation results, the method proves to achieve good performances.

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