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.