Volume 11 • Issue 1 • PP: 56–67 • 2026
An Enhanced LSTM Framework for Solar Radiation Forecasting Using Hybrid Grey Wolf–Waterwheel Plant Optimization
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© 2026 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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Accurate solar radiation prediction is critical for optimizing renewable energy utilization, yet traditional machinelearning models 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.
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References
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