A Method of Load Forecasting Based on Temporal Convolutional Network

2021 ◽  
Author(s):  
Ganghong Zhang ◽  
Wenbin Chen ◽  
Chao Huo ◽  
Huifeng Bai ◽  
Jian Gao ◽  
...  
Keyword(s):  
Load Forecasting ◽  
Convolutional Network

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.

Confidence Interval Estimating Method for Load Forecasting with Consideration of Error Causes

2016 ◽  
Vol 136 (6) ◽  
pp. 775-783
Author(s):  
Naoto Ishibashi ◽  
Akihiro Kabasawa ◽  
Tatsuya Iizaka ◽  
Tohru Katsuno
Keyword(s):  
Confidence Interval ◽  
Load Forecasting

Consumption based-only load forecasting for individual households in nanogrids: a case study

2020 ◽  
Author(s):  
Martina Caliano ◽  
Amedeo Buonanno ◽  
Giorgio Graditi ◽  
Antonino Pontecorvo ◽  
Gianluca Sforza ◽  
...  
Keyword(s):  
Load Forecasting

Support vector approach using data augmentation with copula function applied to load forecasting

2019 ◽  
Author(s):  
Gabriel Ribeiro ◽  
Marcos Yamasaki ◽  
Helon Vicente Hultmann Ayala ◽  
Leandro Coelho ◽  
Viviana Mariani
Keyword(s):  
Data Augmentation ◽  
Load Forecasting ◽  
Copula Function ◽  
Support Vector ◽  
Using Data ◽  
Vector Approach
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