EEMD-GA-LSTM: An innovative approach for daily solar radiation estimation in southern of Algeria

Ahlam Senouci (), Ali Benatiallah (), Djelloul Benatiallah (), Kada Bouchouicha () and Rachid Maouedj ()

Edelweiss Applied Science and Technology, 2025, vol. 9, issue 1, 278-291

Abstract: In a solar-rich area, like the Saharan climate in southern Algeria, to optimize all diversions of use of this energy to the maximum, it is necessary to accurately evaluate the radiation received from the sun. To improve prediction accuracy of daily solar radiation, we present in this paper a new synergistic model that combines three powerful techniques LSTM (Long Short-Term Memory Network), GA (Genetic Algorithm) and EEMD (Ensemble Empirical Mode Decomposition). EEMD is a technique that breaks down complex and highly non-linear solar radiation data into smaller and more manageable components to identify hidden trends, patterns. The GA is used to optimize hyperparameters of LSTM network so that time relevance can be well captured in the solar radiation data. The EEMD-GA-LSTM model was tested in several southern Algerian regions (Biskra, Tamanrasset, Adrar and Tindouf) with different climates. As compared to other existing models including ANN-GA, ANN-PSO, ANFIS-GA and ANFIS-PSO our method performed markedly good R² values and lower RMSE values (RMSE from 3.0125% to 1.554%). The findings underline the model's robustness and reliability in solar radiation prediction, providing valuable information for renewable energy assessment in arid and semi-arid regions. The current study shows the benefit of hybrid models combining metaheuristic optimization and deep learning in a complex environmental data set analysis, paving pathways for future work in both solar energy fields and climate prediction.

Keywords: Genetic algorithm; Ensemble empirical mode decomposition; Long short-term memory network; Metaheuristic optimization. (search for similar items in EconPapers)
Date: 2025
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