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

Rong Liu; Luan Chen

doi:10.1088/1742-6596/2078/1/012051

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

Rong Liu Luan Chen

Year: 2021 Journal: Journal of Physics Conference Series Vol: 2078 (1)Pages: 012051-012051 Publisher: IOP Publishing

DOI: 10.1088/1742-6596/2078/1/012051

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Abstract

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.

Keywords:

Computer science Graph Term (time) Power grid Power network Node (physics) Spatial network Power (physics) Electric power system Theoretical computer science Mathematics Engineering

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Traffic Prediction and Management Techniques

Physical Sciences → Engineering → Building and Construction

Energy Load and Power Forecasting

Physical Sciences → Engineering → Electrical and Electronic Engineering

Time Series Analysis and Forecasting

Physical Sciences → Computer Science → Signal Processing

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

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