Multi-Step-Ahead Electricity Price Forecasting Based on Temporal Graph Convolutional Network

Traditional electricity price forecasting tends to adopt time-domain forecasting methods based on time series, which fail to make full use of the regional information of the electricity market, and ignore the extra-territorial factors affecting electricity price within the region under cross-regiona...

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Veröffentlicht in:	Mathematics (Basel) Jg. 10; H. 14; S. 2366
Hauptverfasser:	Su, Haokun, Peng, Xiangang, Liu, Hanyu, Quan, Huan, Wu, Kaitong, Chen, Zhiwen
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.07.2022
Schlagworte:	Artificial intelligence Artificial neural networks Data analysis Deep learning Electricity electricity price forecasting Electricity pricing Forecasting graph convolutional network Mathematical models Mathematics Neural networks Prediction models Prices spatio-temporal forecasting algorithm Splicing Supply & demand temporal convolutional network Time domain analysis Time series
ISSN:	2227-7390, 2227-7390
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Abstract	Traditional electricity price forecasting tends to adopt time-domain forecasting methods based on time series, which fail to make full use of the regional information of the electricity market, and ignore the extra-territorial factors affecting electricity price within the region under cross-regional transmission conditions. In order to improve the accuracy of electricity price forecasting, this paper proposes a novel spatio-temporal prediction model, which is combined with the graph convolutional network (GCN) and the temporal convolutional network (TCN). First, the model automatically extracts the relationships between price areas through the graph construction module. Then, the mix-jump GCN is used to capture the spatial dependence, and the dilated splicing TCN is used to capture the temporal dependence and forecast electricity price for all price areas. The results show that the model outperforms other models in both one-step forecasting and multi-step forecasting, indicating that the model has superior performance in electricity price forecasting.
AbstractList	Traditional electricity price forecasting tends to adopt time-domain forecasting methods based on time series, which fail to make full use of the regional information of the electricity market, and ignore the extra-territorial factors affecting electricity price within the region under cross-regional transmission conditions. In order to improve the accuracy of electricity price forecasting, this paper proposes a novel spatio-temporal prediction model, which is combined with the graph convolutional network (GCN) and the temporal convolutional network (TCN). First, the model automatically extracts the relationships between price areas through the graph construction module. Then, the mix-jump GCN is used to capture the spatial dependence, and the dilated splicing TCN is used to capture the temporal dependence and forecast electricity price for all price areas. The results show that the model outperforms other models in both one-step forecasting and multi-step forecasting, indicating that the model has superior performance in electricity price forecasting.
Author	Peng, Xiangang Quan, Huan Wu, Kaitong Chen, Zhiwen Su, Haokun Liu, Hanyu
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SubjectTerms	Artificial intelligence Artificial neural networks Data analysis Deep learning Electricity electricity price forecasting Electricity pricing Forecasting graph convolutional network Mathematical models Mathematics Neural networks Prediction models Prices spatio-temporal forecasting algorithm Splicing Supply & demand temporal convolutional network Time domain analysis Time series
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Title	Multi-Step-Ahead Electricity Price Forecasting Based on Temporal Graph Convolutional Network
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