Comparative Analysis of Machine Learning Techniques in Predicting Wind Power Generation: A Case Study of 2018–2021 Data from Guatemala

Abstract

The accurate forecasting of wind power has become a crucial task in renewable energy due to its inherent variability and uncertainty. This study addresses the challenge of predicting wind power generation without meteorological data by utilizing machine learning (ML) techniques on data from 2018 to 2021 from three wind farms in Guatemala. Various machine learning models, including Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), Bagging, and Extreme Gradient Boosting (XGBoost), were evaluated to determine their effectiveness. The performance of these models was assessed using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) metrics. Time series cross-validation was employed to validate the models, with GRU, LSTM, and BiLSTM showing the lowest RMSE and MAE. Furthermore, the Diebold–Mariano (DM) test and Bayesian model comparison were used for pairwise comparisons, confirming the robustness and accuracy of the top-performing models. The results highlight the superior accuracy and robustness of advanced neural network architectures in capturing the complex temporal dependencies in wind power data, making them the most reliable models for precise forecasting. These findings provide critical insights for enhancing grid management and operational planning in the renewable energy sector.