  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.
