Open AccessShort‐term electricity price forecasting based on graph convolution network and attention mechanism
Author(s) -
Yang Yuyun,
Tan Zhenfei,
Yang Haitao,
Ruan Guangchun,
Zhong Haiwang,
Liu Fengkui
Publication year - 2022
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
eISSN - 1752-1424
pISSN - 1752-1416
DOI - 10.1049/rpg2.12413
Subject(s) - computer science , bidding , electricity market , graph , convolution (computer science) , term (time) , electricity , artificial intelligence , data mining , artificial neural network , theoretical computer science , physics , marketing , quantum mechanics , electrical engineering , business , engineering
In electricity markets, locational marginal price (LMP) forecasting is particularly important for market participants in making reasonable bidding strategies, managing potential trading risks, and supporting efficient system planning and operation. Unlike existing methods that only consider LMPs' temporal features, this paper tailors a spectral graph convolutional network (GCN) to greatly improve the accuracy of short‐term LMP forecasting. A three‐branch network structure is then designed to match the structure of LMPs' compositions. Such kind of network can extract the spatial‐temporal features of LMPs, and provide fast and high‐quality predictions for all nodes simultaneously. The attention mechanism is also implemented to assign varying importance weights between different nodes and time slots. Case studies based on the IEEE‐118 test system and real‐world data from the PJM validate that the proposed model outperforms existing forecasting models in accuracy, and maintains a robust performance by avoiding extreme errors.