| Optimizing LSTM Networks for Solar Radiation Forecasting Using Hybrid Water Whale Plant Algorithm |
| Paper ID : 1005-ICEEM2025 (R1) |
| Authors |
|
Abdelhameed Ibrahim1, Amal H. Alharbi2, Amel Ali Alhussan3, Marwa M. Eid4, Mahmoud Elshabrawy Mohamed5, El-Sayed M. El-Kenawy *6 1Computer Engineering and Control Systems Dept. Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt 2Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia 3College of Computer and Information Sciences Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia 4Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 11152, Egypt 5Computer Engineering and Control Systems Dept. Faculty of Engineering, Mansoura University, Egypt mshabrawy@std.mans.edu.eg 6Delta Higher Institute for Engineering and Technology |
| Abstract |
| Accurate solar radiation prediction is crucial for optimizing renewable energy utilization, yet traditional machine learning models often struggle with suboptimal forecasting accuracy. To address this challenge, we propose a novel hybrid optimization algorithm, the Grey Wolf and Water Whale Plant Optimizer (GWWWPA), to enhance Long Short-Term Memory (LSTM) networks for improved solar radiation forecasting. The proposed method leverages the exploration-exploitation synergy of the Grey Wolf Optimizer (GWO) and Water Whale Plant Algorithm (WWPA) to optimize LSTM hyperparameters effectively. Experimental results on the NASA Space Apps Moscow dataset demonstrate that GWWWPA-LSTM outperforms existing optimization techniques, achieving the lowest Mean Squared Error (MSE) of 0.00019392 and the highest R2 score of 0.917262, surpassing standalone GWO, WWPA, PSO, and WOA. These findings highlight the potential of hybrid metaheuristic approaches in enhancing predictive accuracy, facilitating more reliable solar energy forecasting, and supporting sustainable energy management systems. The versatility of the GWWWPA algorithm positions it as a valuable tool for enhancing the performance of deep learning models in a wide range of renewable energy forecasting applications. |
| Keywords |
| Hybrid Optimization, Grey Wolf Optimizer, Water Whale Plant Algorithm, Solar Radiation Prediction, Long Short-Term Memory (LSTM) |
| Status: Accepted |