Ensuring the reliability of wind energy as a dependable source requires overcoming challenges posed by the inherent volatility and stochastic nature of wind patterns.Long-term forecasting provides strategic advantages in managing energy generation projects

enabling the development of effective portfolio management strategies.The primary objective of this study was the development of forecasting methods to support strategic decision-making within the scope of wind energy operations

specifically targeting the Pindaí Wind Complex and its commercial dispatch.The study integrated Big Data analytics

data engineering

and computational techniques through the application of machine learning algorithms: including eXtreme Gradient Boosting

Multilayer Perceptron

Support Vector Regression

Ridge Regression

and Random Forests

aiming to generate forward-looking projections of the complex’s energy production for the year 2023.To this end

five supervised machine learning techniques were modeled and implemented.These techniques were grounded in their respective mathematical and structural formulations

and the empirical foundation for modeling was provided by historical power generation data from the Pindaí Wind Complex

combined with high-resolution realized and forecasted meteorological data retrieved via the Open-Meteo API.The models are trained using historical monthly generation data from the Pindaí Wind Complex

which has an installed capacity of 79.9 MW and is located in the northeastern region of Brazil

along with meteorological data from reanalysis models

such as air temperature

relative humidity

precipitation

surface pressure

wind speed at 10 m

wind speed at 100 m

and wind gusts.These methodologies are applied to forecast monthly wind generation for the year 2023

and the outputs are systematically compared using evaluation metrics to determine the most suitable modeling approach.The results highlight the superiority of the Multilayer Perceptron

Support Vector Regression

and eXtreme Gradient Boosting models

which achieved Kling-Gupta Efficiency (KGE) of 0.89

0.89

and 0.90

mean absolute scaled error (MASE) of 0.29

0.31

and 0.18

root mean square errors (RMSE) of 0.56

0.59

and 0.35

and mean absolute errors (MAE) of 0.48

0.52

and 0.29

respectively.