Assessing the Effects of Extreme Events on Machine Learning Models for Electricity Price Forecasting

João Borges, João Borges, Rui Maia, Sérgio Guerreiro, Sérgio Guerreiro

2023

Abstract

Forecasting electricity prices in the face of extreme events, including natural disasters or abrupt shifts in demand, is a difficult challenge given the volatility and unpredictability of the energy market. Traditional methods of price forecasting may not be able to accurately predict prices under such conditions. In these situations, machine learning algorithms can be used to forecast electricity prices more precisely. By training a machine learning model on historical data, including data from extreme events, it is possible to make more accurate predictions about future prices. This can assist in ensuring the stability and dependability of the electricity market by assisting electricity producers and customers in making educated decisions regarding their energy usage and generation. Accurate price forecasting can also lessen the likelihood of financial losses for both producers and consumers during extreme events. In this paper, we propose to study the effects of machine learning algorithms in electricity price forecasting, as well as develop a forecasting model that excels in accurately predicting said variable under the volatile conditions of extreme events.

Paper Citation

in Harvard Style

Borges J., Maia R. and Guerreiro S. (2023). Assessing the Effects of Extreme Events on Machine Learning Models for Electricity Price Forecasting. In Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-648-4, SciTePress, pages 683-690. DOI: 10.5220/0012038700003467

in Bibtex Style

@conference{iceis23,
author={João Borges and Rui Maia and Sérgio Guerreiro},
title={Assessing the Effects of Extreme Events on Machine Learning Models for Electricity Price Forecasting},
booktitle={Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2023},
pages={683-690},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012038700003467},
isbn={978-989-758-648-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Assessing the Effects of Extreme Events on Machine Learning Models for Electricity Price Forecasting
SN - 978-989-758-648-4
AU - Borges J.
AU - Maia R.
AU - Guerreiro S.
PY - 2023
SP - 683
EP - 690
DO - 10.5220/0012038700003467
PB - SciTePress