Frederico A. F. Almeida
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Frederico A. F. Almeida has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Carlos Rodrigues
Faculty of Electrical and Biomedical Engineering, Federal University of Pará, Belém 66075-110, Brazil
1 shared publication - Flávia Pessoa Monteiro
Doutora em Engenharia Elétrica Instituição Universidade Federal do Oeste do Pará (UFOPA) Endereço: Oriximiná Pará Brasil
1 shared publication - Josivan R. Reis
Oriximiná Campus, Federal University of Western Pará, Oriximiná 68270-000, Brazil
1 shared publication - Maria Emília de Lima Tostes
Post-Graduate Program in Electrical Engineering, Universidade Federal do Pará (UFPA), Belém 66075-110, PA, Brazil
1 shared publication - Suzane Aquino Monteiro
Capitão Poço Campus, Federal Rural University of the Amazon, Capitão Poço 68650-000, Brazil
1 shared publication - Ubiratan Holanda Bezerra
Post-Graduate Program in Electrical Engineering, Universidade Federal do Pará (UFPA), Belém 66075-110, PA, Brazil
1 shared publication
Latest Publications
- Open Access
A Hybrid Methodology Using Machine Learning Techniques and Feature Engineering Applied to Time Series for Medium- and Long-Term Energy Market Price Forecasting
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.
Open AccessA Hybrid Methodology Using Machine Learning Techniques and Feature Engineering Applied to Time Series for Medium- and Long-Term Energy Market Price Forecasting
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.