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Adaptive Behaviour of Metaheuristic Algorithms in Solar Radiation Prediction: Influence of Input Variable Informativeness
Authors:
Ali Khazzar1
, Djelloul Benatiallah1
, Kada Bouchouicha2
1LDDI Laboratory, Department of Material Sciences, Faculty of Material Sciences, Mathematics and
Computer Science, University Ahmed Draia, Adrar, Algeria
2Center for Renewable Energy Development (CDER), Bouzareah, Algiers, Algeria
Received: 4 June 2025
Revised: 20 March 2026
Accepted: 4 April 2026
Published: 29 June 2026
Abstract:
This study focuses on short-term (10-minute) forecasting of Global Horizontal Irradiance (GHI) using artificial neural networks (ANNs) enhanced by three metaheuristic optimization algorithms: the FireFly Algorithm (FFA), Particle Swarm Optimization (PSO), and the White Whale Optimization Algorithm (WWOA). The models were trained using meteorological and astronomical data collected from a monitoring station in Khenchela, Algeria. To identify the solar radiation component most strongly correlated with GHI, one additional radiometric input, selected from the available components, was introduced in separate experiments. Model performance was assessed using standard statistical metrics: relative Root Mean Squared Error (rRMSE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R²). Results indicate that meteorological and astronomical variables alone are insufficient for highly accurate GHI prediction. However, when Global Tilted Irradiance (GTI) was incorporated as an additional input, all three hybrid models exhibited significantly improved accuracy. The ANN-FFA model achieved the best performance, with rRMSE=3.73%, MAPE=6.07%, and R²=0.9962. These findings demonstrate that GTI is the solar radiation component most closely related to GHI under the studied conditions. Furthermore, the study confirms the effectiveness of FFA, PSO, and WWOA in optimizing ANN hyperparameters for solar irradiance forecasting, with FFA yielding the most robust results.
Keywords:
Renewable energy, Solar energy system, GHI, ANN, Metaheuristic algorithms, Predictions
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© 2026 by the authors. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)
Volume 11
Number 2
June 2026
Last Edition
Volume 11
Number 1
March 2026
How to Cite
A. Khazzar, D. Benatiallah, K. Bouchouicha, Adaptive Behaviour of Metaheuristic Algorithms in Solar Radiation Prediction: Influence of Input Variable Informativeness. Applied Engineering Letters, 11(2), 2026: 71-83.
https://doi.org/10.46793/aeletters.2026.11.2.2