# Short-Term Electricity Price Forecasting based on Graph Convolution Network and Attention Mechanism

arXiv: https://arxiv.org/abs/2107.12794 · HTML: https://mlsanity.com/paper/2107.12794/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 46 references that are in the map.
- First posted on arXiv 26 Jul 2021.

## Builds on (most cited first, 3 of 3)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [Review of Learning-Assisted Power System Optimization](https://mlsanity.com/paper/2007.00210/) (2020)

## Built on by (most cited first, 1 of 1)

- [Deep Time-Series Models Meet Volatility: Multi-Horizon Electricity Price Forecasting in the Australian National Electricity Market](https://mlsanity.com/paper/2602.01157/) (2026)

## Abstract (arXiv)

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.