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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Vydané v:Mathematics (Basel) Ročník 10; číslo 14; s. 2366
Hlavní autori: Su, Haokun, Peng, Xiangang, Liu, Hanyu, Quan, Huan, Wu, Kaitong, Chen, Zhiwen
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.07.2022
Predmet:
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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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:2227-7390
2227-7390
DOI:10.3390/math10142366