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

Berny Carrera and Kwanho Kim ()
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Berny Carrera: Department of Industrial and Systems Engineering, Dongguk University, Seoul 04620, Republic of Korea
Kwanho Kim: Department of Industrial and Systems Engineering, Dongguk University, Seoul 04620, Republic of Korea

Energies, 2024, vol. 17, issue 13, 1-27

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.

Keywords: wind power forecasting; deep learning; machine learning; grid management; renewable energy; smart grids; meteorological data absence; Diebold–Mariano test; Bayesian model comparison (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: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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