Article
A Bayesian Optimization-Based LSTM Model for Wind Power Forecasting in the Adama District, Ethiopia
Author/s | Tefera Habtemariam, Ejigu
Kekeba, Kula Martínez Ballesteros, María del Mar Martínez Álvarez, Francisco |
Department | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos |
Publication Date | 2023-02 |
Deposit Date | 2024-04-11 |
Published in |
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Abstract | Renewable energies, such as solar and wind power, have become promising sources of energy to address the increase in greenhouse gases caused by the use of fossil fuels and to resolve the current energy crisis. Integrating ... Renewable energies, such as solar and wind power, have become promising sources of energy to address the increase in greenhouse gases caused by the use of fossil fuels and to resolve the current energy crisis. Integrating wind energy into a large-scale electric grid presents a significant challenge due to the high intermittency and nonlinear behavior of wind power. Accurate wind power forecasting is essential for safe and efficient integration into the grid system. Many prediction models have been developed to predict the uncertain and nonlinear time series of wind power, but most neglect the use of Bayesian optimization to optimize the hyperparameters while training deep learning algorithms. The efficiency of grid search strategies decreases as the number of hyperparameters increases, and computation time complexity becomes an issue. This paper presents a robust and optimized long-short term memory network for forecasting wind power generation in the day ahead in the context of Ethiopia’s renewable energy sector. The proposal uses Bayesian optimization to find the best hyperparameter combination in a reasonable computation time. The results indicate that tuning hyperparameters using this metaheuristic prior to building deep learning models significantly improves the predictive performances of the models. The proposed models were evaluated using MAE, RMSE, and MAPE metrics, and outperformed both the baseline models and the optimized gated recurrent unit architecture. |
Funding agencies | Ministerio de Ciencia e Innovación (MICIN). España Junta de Andalucía |
Project ID. | PID2020-117954RB
TED2021-131311B-C22 PY20-00870 UPO-138516 |
Citation | Tefera Habtemariam, E., Kekeba, K., Martínez Ballesteros, M.d.M. y Martínez Álvarez, F. (2023). A Bayesian Optimization-Based LSTM Model for Wind Power Forecasting in the Adama District, Ethiopia. Energies, 16 (5), 1-22. https://doi.org/10.3390/en16052317. |
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