Methodology for Multi-Step Forecasting of Electricity Spot Prices Based on Neural Networks Applied to the Brazilian Energy Market

Marianna B. B. Dias (), George R. S. Lira and Victor M. E. Freire
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Marianna B. B. Dias: Center of Electrical Engineering and Informatics, Federal University of Campina Grande, Campina Grande 58429900, Brazil
George R. S. Lira: Department of Electrical Engineering, Federal University of Campina Grande, Campina Grande 58429900, Brazil
Victor M. E. Freire: Center of Electrical Engineering and Informatics, Federal University of Campina Grande, Campina Grande 58429900, Brazil

Energies, 2024, vol. 17, issue 8, 1-14

Abstract: Forecasting electricity spot prices holds paramount significance for informed decision-making among energy market stakeholders. This study introduces a methodology utilizing a multilayer perceptron (MLP) neural network for multivariate electricity spot price prediction. The model underwent a feature selection process to identify the most influential predictors. In the validation phase, the model’s performance was evaluated using key metrics, including trend accuracy percentage index (TAPI), normalized root mean squared error (NRMSE), and mean absolute percentage error (MAPE). The results were obtained for a four-week forecast horizon in order to serve as an auxiliary tool to facilitate decision-making processes in the short-term energy market. The relevance of short-term electricity spot price forecasting lies in its direct impact on pricing strategies during energy contract negotiations, which allows for the making of assertive decisions in the energy trading landscape.

Keywords: electricity market; electricity price forecasting; electricity trading; multilayer perceptron (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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