Authors:
João Borges
1
;
2
;
Rui Maia
1
and
Sérgio Guerreiro
2
;
1
Affiliations:
1
Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal
;
2
INESC-ID, Rua Alves Redol 9, 1000-029 Lisbon, Portugal
Keyword(s):
Extreme Events, Electricity Price Forecasting, Machine Learning.
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 a
lgorithms 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.
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