An Intra-Day Electricity Price Forecasting Based on a Probabilistic Transformer Neural Network Architecture

Sergio Cantillo-Luna, Ricardo Moreno-Chuquen, Jesus Lopez-Sotelo, David Celeita

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

This paper describes the development of a deep neural network architecture based on transformer encoder blocks and Time2Vec layers for the prediction of electricity prices several steps ahead (8 h), from a probabilistic approach, to feed future decision-making tools in the context of the widespread use of intra-day DERs and new market perspectives. The proposed model was tested with hourly wholesale electricity price data from Colombia, and the results were compared with different state-of-the-art forecasting baseline-tuned models such as Holt–Winters, XGBoost, Stacked LSTM, and Attention-LSTM. The findings show that the proposed model outperforms these baselines by effectively incorporating nonlinearity and explicitly modeling the underlying data’s behavior, all of this under four operating scenarios and different performance metrics. This allows it to handle high-, medium-, and low-variability scenarios while maintaining the accuracy and reliability of its predictions. The proposed framework shows potential for significantly improving the accuracy of electricity price forecasts, which can have significant benefits for making informed decisions in the energy sector.

Original languageEnglish (US)
Article number6767
JournalEnergies
Volume16
Issue number19
DOIs
StatePublished - Oct 2023

All Science Journal Classification (ASJC) codes

  • Renewable Energy, Sustainability and the Environment
  • Fuel Technology
  • Engineering (miscellaneous)
  • Energy Engineering and Power Technology
  • Energy (miscellaneous)
  • Control and Optimization
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'An Intra-Day Electricity Price Forecasting Based on a Probabilistic Transformer Neural Network Architecture'. Together they form a unique fingerprint.

Cite this