A Bayesian Optimization-Based LSTM Model for Wind Power Forecasting in the Adama District, Ethiopia

Ejigu Tefera Habtemariam, Kula Kekeba, María Martínez-Ballesteros and Francisco Martínez-Álvarez ()
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Ejigu Tefera Habtemariam: Big Data and HPC Center of Excellence, Department of Software Engineering, Addis Ababa Science & Technology University, Addis Ababa P.O. Box 16417, Ethiopia
Kula Kekeba: Big Data and HPC Center of Excellence, Department of Software Engineering, Addis Ababa Science & Technology University, Addis Ababa P.O. Box 16417, Ethiopia
María Martínez-Ballesteros: Department of Computer Science, University of Seville, ES-41012 Seville, Spain
Francisco Martínez-Álvarez: Data Science & Big Data Lab, Pablo de Olavide University, ES-41013 Seville, Spain

Energies, 2023, vol. 16, issue 5, 1-22

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 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.

Keywords: Bayesian optimization; deep learning; LSTM; time series; forecasting (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: 2023
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