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Hybrid Deep Learning Enabled Load Prediction for Energy Storage Systems

Firas Abedi1, Hayder M. A. Ghanimi2, Mohammed A. M. Sadeeq3, Ahmed Alkhayyat4,*, Zahraa H. Kareem5, Sarmad Nozad Mahmood6, Ali Hashim Abbas7, Ali S. Abosinnee8, Waleed Khaild Al-Azzawi9, Mustafa Musa Jaber10,11, Mohammed Dauwed12

1 Department of Mathematics, College of Education, Al-Zahraa University for Women, Karbala, Iraq
2 Biomedical Engineering Department, College of Engineering, University of Warith Al-Anbiyaa, Karbala, Iraq
3 ITM Department, Technical College of Administration, Duhok Polytechnic University, Duhok, Iraq
4 College of Technical Engineering, The Islamic University, Najaf, Iraq
5 Department of Medical Instrumentation Techniques Engineering, Al-Mustaqbal University College, Hillah, 51001, Iraq
6 Computer Technology Engineering, College of Engineering Technology, Al-Kitab University, Kirkuk, 36013, Iraq
7 College of Information Technology, Imam Ja’afar Al-Sadiq University, Al-Muthanna, 66002, Iraq
8 Altoosi University College, Najaf, Iraq
9 Department of Medical Instruments Engineering Techniques, Al-Farahidi University, Bagdad, Iraq
10 Department of Medical Instruments Engineering Techniques, Al-Turath University College, Baghdad, 10021, Iraq
11 Department of Medical Instruments Engineering Techniques, Al-Farahidi University, Baghdad, 10021, Iraq
12 Department of Medical Instrumentations Techniques Engineering, Dijlah University College, Baghdad, Iraq

* Corresponding Author: Ahmed Alkhayyat. Email: email

TSP_CMC_34221.pdf

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