A Hybrid Methodology Using Machine Learning Techniques and Feature Engineering Applied to Time Series for Medium- and Long-Term Energy Market Price Forecasting

Flávia Pessoa Monteiro (), Suzane Monteiro, Carlos Rodrigues, Josivan Reis, Ubiratan Bezerra, Maria Emília Tostes and Frederico A. F. Almeida
Additional contact information
Flávia Pessoa Monteiro: Oriximiná Campus, Federal University of Western Pará, Oriximiná 68270-000, Brazil
Suzane Monteiro: Capitão Poço Campus, Federal Rural University of the Amazon, Capitão Poço 68650-000, Brazil
Carlos Rodrigues: Faculty of Electrical and Biomedical Engineering, Federal University of Para, Belém 66075-110, Brazil
Josivan Reis: Oriximiná Campus, Federal University of Western Pará, Oriximiná 68270-000, Brazil
Ubiratan Bezerra: Faculty of Electrical and Biomedical Engineering, Federal University of Para, Belém 66075-110, Brazil
Maria Emília Tostes: Faculty of Electrical and Biomedical Engineering, Federal University of Para, Belém 66075-110, Brazil
Frederico A. F. Almeida: Eletrobras, Rio de Janeiro 20091-005, Brazil

Energies, 2025, vol. 18, issue 6, 1-26

Abstract: In the electricity market, the issue of contract negotiation prices between generators/traders and buyers is of particular relevance, as an accurate contract modeling leads to increased financial returns and business sustainability for the various participating agents, encouraging investments in specialized sectors for price forecasting and risk analysis. This paper presents a methodology applied in experiments on energy forward curve scenarios using a set of techniques, including Long Short-Term Memory (LSTM), Extreme Gradient Boosting (XGBoost), Seasonal AutoRegressive Integrated Moving Average with eXogenous regressors (SARIMAX), and Feature Engineering to generate a 10-year projection of the Conventional Long-Term Price. The model validation proved to be effective, with errors of only 4.5% by Root Mean Square Error (RMSE) and slightly less than 2% by Mean Absolute Error (MAE), for a time series spanning from 7 January 2012 to 31 August 2024, in the Brazilian energy market.

Keywords: energy market forecasting; machine learning; time series decomposition; hybrid modeling; long-term energy price projection; feature engineering; LSTM; SARIMAX; XGBoost (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: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/6/1387/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/6/1387/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:6:p:1387-:d:1610009

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().