A Transformer approach for Electricity Price Forecasting

Oscar Llorente, Jose Portela
3/24/2024
cs.LGcs.AI

No official code found. Best implementation: osllogon/epf-transformers (59 stars, MIT, last commit 2024-08-21).

Abstract

This paper presents a novel approach to electricity price forecasting (EPF) using a pure Transformer model. As opposed to other alternatives, no other recurrent network is used in combination to the attention mechanism. Hence, showing that the attention layer is enough for capturing the temporal patterns. The paper also provides fair comparison of the models using the open-source EPF toolbox and provide the code to enhance reproducibility and transparency in EPF research. The results show that the Transformer model outperforms traditional methods, offering a promising solution for reliable and sustainable power system operation.

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Code Implementations(1)

osllogon/epf-transformersCommunity100%
MIT

Official implementation of "A Transformer approach for Electricity Price Forecasting"

599Shell, PythonSep 27, 20222 years agoMIT

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Cite this paper

@article{llorente2024transformer,
  title  = {A Transformer approach for Electricity Price Forecasting},
  author = {Oscar Llorente and Jose Portela},
  year   = {2024},
  eprint = {2403.16108},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2403.16108v3}
}

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