Solar radiation forecasting based on ANN, SVM and a novel hybrid FFA-ANN model: A case study of six cities south of Algeria

Halima Djeldjli; Djelloul Benatiallah; Camel Tanougast; Ali Benatiallah; Halima Djeldjli; Djelloul Benatiallah; Camel Tanougast; Ali Benatiallah

doi:10.3934/energy.2024004

AIMS Energy

2024, Volume 12, Issue 1: 62-83. doi: 10.3934/energy.2024004

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Research article

Solar radiation forecasting based on ANN, SVM and a novel hybrid FFA-ANN model: A case study of six cities south of Algeria

1.
Material Sciences Department, Faculty of Material Sciences, Mathematics and Computer Science Laboratory of Sustainable Development and Computer Science (LSDCS), University Ahmed Draia, Adrar, 01000, Algeria
2.
Laboratory of Systems Design, Optimization and Modeling (LCOMS), University of Lorraine, Metz 57070, France
3.
Laboratory of Energy Environment and Information System (LEEIS), University Ahmed Draia, Adrar, 01000, Algeria

Received: 30 October 2023 Revised: 06 December 2023 Accepted: 11 December 2023 Published: 27 December 2023

This study was conducted for six cities in southern Algeria, where the accuracy of three models—support vector machines (SVM), artificial neural networks (ANN) and a novel hybrid firefly algorithm-based model (FFA-ANN)—were investigated when estimating global solar irradiation throughout an eleven-year period, utilizing nine input parameters as input data. The goal of our novel suggested a hybrid FFA-ANN model, where we relied on the optimization Firefly algorithm to enhance the ANN model created. Despite the fact that the ANN and SVM models produced promising results, our suggested FFA-ANN hybrid model outperformed the stand-alone ANN-based model using three statistical factors—correlation coefficient, relative root mean squared error and mean absolute percent error—with the best values of (R = 0.9321, rRMSE = 9.35% and MAPE = 6.29%). The findings demonstrated that FFA-ANN was preferable to the optimized SVM and ANN models when forecasting daily global solar irradiation in all zones. Furthermore, after comparing the combinations, the study's findings showed that the ANN model depended on: Extraterrestrial solar irradiation (H₀), declination and average temperature (T_avg) together with relative humidity (RH) as inputs in order to estimate daily sun radiation. Thus, the findings of this study suggest that in regions with dry climates and other places with comparable climates, the created model may be used to estimate daily global solar radiation whenever data is accessible.
- hybrid model,
- artificial neural network (ANN),
- firefly algorithm (FFA),
- solar radiation,
- optimization,
- energy systems,
- support vector machine (SVM)
Citation: Halima Djeldjli, Djelloul Benatiallah, Camel Tanougast, Ali Benatiallah. Solar radiation forecasting based on ANN, SVM and a novel hybrid FFA-ANN model: A case study of six cities south of Algeria[J]. AIMS Energy, 2024, 12(1): 62-83. doi: 10.3934/energy.2024004

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Abstract

This study was conducted for six cities in southern Algeria, where the accuracy of three models—support vector machines (SVM), artificial neural networks (ANN) and a novel hybrid firefly algorithm-based model (FFA-ANN)—were investigated when estimating global solar irradiation throughout an eleven-year period, utilizing nine input parameters as input data. The goal of our novel suggested a hybrid FFA-ANN model, where we relied on the optimization Firefly algorithm to enhance the ANN model created. Despite the fact that the ANN and SVM models produced promising results, our suggested FFA-ANN hybrid model outperformed the stand-alone ANN-based model using three statistical factors—correlation coefficient, relative root mean squared error and mean absolute percent error—with the best values of (R = 0.9321, rRMSE = 9.35% and MAPE = 6.29%). The findings demonstrated that FFA-ANN was preferable to the optimized SVM and ANN models when forecasting daily global solar irradiation in all zones. Furthermore, after comparing the combinations, the study's findings showed that the ANN model depended on: Extraterrestrial solar irradiation (H₀), declination and average temperature (T_avg) together with relative humidity (RH) as inputs in order to estimate daily sun radiation. Thus, the findings of this study suggest that in regions with dry climates and other places with comparable climates, the created model may be used to estimate daily global solar radiation whenever data is accessible.

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