Khalid Faten ¹ Doaa Khafaga ¹ El-Sayed M. El-kenawy ^2* Marwa Eid ³ Abdelhameed Ibrahim ⁴ Laith Abualigah ⁵ Nima Khodadadi ⁶ Abdelaziz Abdelhamid ^7*

• ¹ Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia, Riyadh, Egypt
• ² Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, 35111, Egypt, Mansoura, Egypt
• ³ Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura, Egypt, Mansoura, Egypt
• ⁴ School of ICT, Faculty of Engineering, Design and Information Communications Technology (EDICT), Bahrain Polytechnic, PO Box 33349, Isa Town, Bahrain, Bahrain, Bahrain
• ⁵ Artificial Intelligence and Sensing Technologies Research Center, University of Tabuk, Tabuk, Saudi Arabia
• ⁶ Department of Civil and Architectural Engineering, College of Engineering, University of Miami, Coral Gables, Florida, United States
• ⁷ Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra, Riyadh, Saudi Arabia

The stability of smart grids is crucial for ensuring reliable and efficient power distribution in modern energy systems. This paper presents an optimized Long Short-Term Memory model for predicting smart grid stability, leveraging the Novel Guide-Waterwheel Plant Algorithm (Guide-WWPA) for enhanced performance. Traditional methods often struggle with the complexity and dynamic nature of smart grids, necessitating advanced approaches for accurate predictions.The proposed LSTM model, optimized using Guide-WWPA, addresses these challenges by effectively capturing temporal dependencies and nonlinear relationships in the data. The proposed approach involves a comprehensive preprocessing pipeline to handle data heterogeneity and 1 Faten Khalid Karim et al.noise, followed by the implementation of the LSTM model optimized through Guide-WWPA. The Guide-WWPA combines the strength of the WWPA with a novel guidance mechanism, ensuring efficient exploration and exploitation of the search space. The optimized LSTM is evaluated on a real-world smart grid dataset, demonstrating superior performance compared to traditional optimization techniques. Experimental Results indicate significant improvements in prediction accuracy and computational efficiency, highlighting the potential of the Guide-WWPA optimized LSTM for real-time smart grid stability prediction. This work contributes to the development of intelligent energy management systems, offering a robust tool for maintaining grid stability and enhancing overall energy reliability. On the other hand, statistical evaluations were carried out to prove the stability and difference of the proposed methodology. The results of the experiments demonstrate that the Guide-WWPA+LSTM strategy is superior to the other machine learning approaches.