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Mohtashami, Soheila, Aghashariatmadari, Zahra. (1404). A Comparative Analysis of Feed-Forward and Long Short-Term Memory Networks for Solar Radiation Estimation.. , 30(2), 388-416. doi: 10.22059/jdesert.2025.106182
Soheila Mohtashami; Zahra Aghashariatmadari. "A Comparative Analysis of Feed-Forward and Long Short-Term Memory Networks for Solar Radiation Estimation.". , 30, 2, 1404, 388-416. doi: 10.22059/jdesert.2025.106182
Mohtashami, Soheila, Aghashariatmadari, Zahra. (1404). 'A Comparative Analysis of Feed-Forward and Long Short-Term Memory Networks for Solar Radiation Estimation.', , 30(2), pp. 388-416. doi: 10.22059/jdesert.2025.106182
Mohtashami, Soheila, Aghashariatmadari, Zahra. A Comparative Analysis of Feed-Forward and Long Short-Term Memory Networks for Solar Radiation Estimation.. , 1404; 30(2): 388-416. doi: 10.22059/jdesert.2025.106182

A Comparative Analysis of Feed-Forward and Long Short-Term Memory Networks for Solar Radiation Estimation.

Desert
دوره 30، شماره 2، اسفند 2025، صفحه 388-416
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/jdesert.2025.106182
نویسندگان
Soheila Mohtashami1؛ Zahra Aghashariatmadari* 2
1Department of Irrigation & Reclamation Engineering, University of Tehran, Karaj, Alborz Iran.
2Irrigation & Reclamation Engrg. Dept. University of Tehran Karaj, Iran.
چکیده
Accurate estimation of solar radiation is essential for numerous industrial applications, energy management, and agricultural planning. This study investigates the effectiveness of advanced machine learning models for solar radiation prediction in Kerman Province, Iran, utilizing a comprehensive set of meteorological variables. Following rigorous quality control procedures and correlation-based feature selection, the dataset was divided into training (80%) and testing (20%) subsets. Two Neural Networks, namely Long Short-Term Memory (LSTM) with the Adam optimizer and Feed-Forward Neural Network (FFNN), were developed and trained under six input scenarios, employing various learning algorithms including Levenberg–Marquardt (LM), Bayesian Regularization (BR), Gradient Descent (GD), and Resilient Propagation (RP) at both daily and monthly timescales. The results indicate that the FFNN-BR model under scenario 6, incorporating a wide range of meteorological inputs, yielded the highest accuracy for monthly radiation estimation (R2 = 0.92, ARE = 4.5%). For daily radiation prediction, the LSTM model under scenario 4 provided superior performance (R2 = 0.91, ARE = 1.35%). These findings underscore the importance of model selection and input configuration in enhancing solar radiation estimation accuracy, offering valuable insights for renewable energy resource assessment in arid regions.
کلیدواژه‌ها
Solar radiation؛ Artificial Neural Network؛ Long Short-Term Memory؛ Feed-Forward Neural Network؛ Machine Learning Models
آمار
تعداد مشاهده مقاله: 39





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