The novelty based on Energy Management Technologies for Integrated Microgrid frameworks

[1] A. Alsharif, C. W. Tan, R. Ayop, K. Y. Lau, and A. M. Dobi, “A rule-based power management strategy for Vehicle-to-Grid system using antlion sizing optimization,” J. Energy Storage, vol. 41, no. July, p. 102913, Sep. 2021, doi: 10.1016/j.est.2021.102913.

[2] H. Kilicoglu and P. Tricoli, (2023).“Technical Review and Survey of Future Trends of Power Converters for Fast-Charging Stations of Electric Vehicles,” Energies, vol. 16, no. 13, p. 5204, Jul. 2023, doi: 10.3390/en16135204.

[3] M. Shehab, A. T. Khader, and M. A. Al-Betar, “A survey on applications and variants of the cuckoo search algorithm,” Appl. Soft Comput. J., vol. 61, no. March, pp. 1041–1059, 2017, doi: 10.1016/j.asoc.2017.02.034.

[4] H. Yu, S. Niu, Y. Shang, Z. Shao, Y. Jia, and L. Jian, “Electric vehicles integration and vehicle-to-grid operation in active distribution grids: A comprehensive review on power architectures, grid connection standards and typical applications,” Renew. Sustain. Energy Rev., vol. 168, no. April, p. 112812, Oct. 2022, doi: 10.1016/j.rser.2022.112812.

[5] Abdussalam Ali Ahmed, Omar Ahmed Mohamed Edbeib, Aisha Douma, and Ibrahim Imbayah Khalefah Imbayah, “Electric vehicles revolution: The future, challenges, and prospects in the Arab countries,” Glob. J. Eng. Technol. Adv., vol. 6, no. 3, pp. 081–087, Mar. 2021, doi: 10.30574/gjeta.2021.6.3.0040.

[6] Y. X. Huang, F. Yang, Y. Luo, and C. L. Xia, “Power optimization strategy considering electric vehicle in home energy management system,” J. Electron. Sci. Technol., vol. 14, no. 3, 2016, doi: 10.11989/JEST.1674-862X.603161.

[7] M. Khaleel, A. A. Ahmed, and A. Alsharif, “Artificial Intelligence in Engineering,” Brill. Res. Artif. Intell., vol. 3, no. 1, pp. 32–42, Mar. 2023, doi: 10.47709/brilliance.v3i1.2170.

[8] A. A. Ahmed, A. Alsharif, T. Triwiyanto, M. Khaleel, C. W. Tan, and R. Ayop, “Using of Neural Network-Based Controller to Obtain the Effect of Hub Motors Weight on Electric Vehicle Ride Comfort,” in 2022 IEEE 2nd International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering (MI-STA), IEEE, May 2022, pp. 189–192. doi: 10.1109/MI-STA54861.2022.9837608.

[9] A. Alsharif et al., “Impact of Electric Vehicle on Residential Power Distribution Considering Energy Management Strategy and Stochastic Monte Carlo Algorithm,” Energies, vol. 16, no. 3, p. 1358, Jan. 2023, doi: 10.3390/en16031358.

[10] V. H. Fan, Z. Dong, and K. Meng, “Integrated distribution expansion planning considering stochastic renewable energy resources and electric vehicles,” Appl. Energy, vol. 278, 2020, doi: 10.1016/j.apenergy.2020.115720.

[11] K.-S. Na, J. Lee, J.-M. Kim, Y.-S. Lee, J. Yi, and C.-Y. Won, “Power Conversion System Operation Algorithm for Efficient Energy Management of Microgrids,” Electronics, vol. 10, no. 22, p. 2791, Nov. 2021, doi: 10.3390/electronics10222791.

[12] M. F. Zia, E. Elbouchikhi, and M. Benbouzid, “Microgrids energy management systems: A critical review on methods, solutions, and prospects,” Appl. Energy, vol. 222, no. April, pp. 1033–1055, 2018, doi: 10.1016/j.apenergy.2018.04.103.

[13] A. Alsharif, A. A. Ahmed, M. M. Khaleel, A. S. D. Alarga, O. S. M. Jomah, and A. B. E. Alrashed, “Stochastic Method and Sensitivity Analysis Assessments for Vehicle-to-Home Integration based on Renewable Energy Sources,” in 2023 IEEE 3rd International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering (MI-STA), IEEE, May 2023, pp. 783–787. doi: 10.1109/MI-STA57575.2023.10169210.

[14] Fatimah, M, Yousuf Mohammed, Llahm Ben Dalla, Ahmed Abuzaraida, Abdulgader Alsharif , Hala J. El-Khozondar .(2026). Assessing Climate Variables and Solar Energy Potential Using Geographic Information Systems (GIS) and Predictive Machine Learning Frameworks for Sustainable Development in Al Bayda, Libya: A Study in Educational Environmental Geography, 8th International Conference on Frontiers in Academic Research, July 16-17, 2026: Konya, Turkey, © 2026 Published by All Sciences Academy.

[15] Ben Dalla, L., Karal, Ömer, EL-Sseid, M., & Alsharif, A. (2026). An IoT-Enabled, THD-Based Fault Detection and Predictive Maintenance Framework for Solar PV Systems in Harsh Climates: Integrating DFT and Machine Learning for Enhanced Performance and Resilience. Wadi Alshatti University Journal of Pure and Applied Sciences, 4(1), 41-55.

[16] El-Khozondar, H. J., Nassar, Y. F., El-Khozondar, R. J., Elnaggar, M., Salah, W. A., Rekik, S., ... & Dalla, L. O. B. (2027). Analysis of a grid/wind/hydrogen hybrid energy system for producing electricity, vehicle fuel and freshwater for Gaza Strip-Palestine. Fuel, 428, 140358.

[17] Alrawayati, H. A. W. A., & Tökeşer, Ü. M. I. T. (2021). PARKINSON’S DISEASE DIAGNOSIS BASED ON THE CONVOLUTIONAL NEURAL NETWORK AND PARTICLE SWARM OPTIMIZATION ALGORITHM. Asian Journal of Mathematics and Computer Research, 28(1), 26-37.

[18] Ben Dalla L., Jetlaweib S.S., Karal Ö., EL-sseid M., Medeni T.D. (2026). Informal Economy Dynamics in Central Africa and the Mediterranean: A Machine Learning Approach. Economy: strategy and practice. 2026;21(2):6-22. https://doi.org/10.51176/1997-9967-2026-2-6-22

[19] Karaoglu, A. N., Caglar, H., Degirmenci, A., & Karal, O. (2021, September). Performance improvement with decision tree in predicting heart failure. In 2021 6th International Conference on Computer Science and Engineering (UBMK) (pp. 781-784). IEEE.‏

[20] Karal, Ö. (2024). Comparative performance analysis of epsilon-insensitive and pruningbased algorithms for sparse least squares support vector regression. Sigma Journal of Engineering and Natural Sciences, 42(2), 578-589.‏

[21] Karal, O. (2023). Robust and optimal epsilon-insensitive Kernel-based regression for general noise models. Engineering Applications of Artificial Intelligence, 120, 105841.‏

[22] Osman, M., Elghaffi, F., Dalla, L. B., Karal, Ö., & Rashid, T. (2026). A New Approach for Low-Latency, High-Accuracy Anomaly Detection at the Edge: Benchmarking Quantized Autoencoders, LSTMs, and Lightweight Transformers on RT-IoT2022 Time-Series Traffic. Wadi Alshatti University Journal of Pure and Applied Sciences, 110-121.‏

[23] Kardaş, F., & Karal, Ö. (2021, October). Wireless indoor localization problem with artificial neural network. In 2021 Innovations in Intelligent Systems and Applications Conference (ASYU) (pp. 1-5). IEEE.‏

[24] Muslu, E. A., & Karal, Ö. (2021, June). Mathematical Modeling of Threats in Electronic Warfare Systems. In 2021 29th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE.‏

[25] Kardaş, F., & Karal, Ö. (2021, October). Wireless indoor localization problem with artificial neural network. In 2021 Innovations in Intelligent Systems and Applications Conference (ASYU) (pp. 1-5). IEEE.‏

[26] lrawayati, H. (2020). Development of High Efficiency Optimization Algorithm based on New Topology in Particle Swarm Optimization for Parkinson’s. Environment, 9, 10.

[27] Dalla, L. O. B., Karal, Ö., Degirmenci, A., EL-sseid, M. A. M., Essgaer, M., & Alsharif, A. (2025). A comprehensive literature review (LR) on optimization algorithms of sewage water treatment processes. Comprehensive Science Journal, 10(37), 3204-3220

[28] Danisman, K. E. N. A. N., Dalkiran, I., & Celebi, F. V. (2006). Design of a high precision temperature measurement system based on artificial neural network for different thermocouple types. Measurement, 39(8), 695-700.‏

[29] YIĞIT, S., ERYIĞIT, R., & Celebi, F. V. (2011). Optical gain model proposed with the use of artificial neural networks optimised by artificial bee colony algorithm. Optoelectronics and Advanced Materials-Rapid Communications, 5(September 2011), 1026-1029.‏

[30] Ramadhan, H. R. S., Osman, M. O. M., Dalla, L. O. F. B., Rashid, T. A., Albaraesi, M. J. S., El-Sseid, M. A. M., & Alnnale, T. (2025). A New Approach of the Machine Learning Framework Integrating Policy Design to Predict Renewable Electricity Penetration in Resource-Constrained Settings. Comprehensive Science Journal, 10(Supplement 38), 2929-2950

[31] Dalla, L. O. B., KARAL, Ö., Degirmenci, A., CANBOLAT, H., ÇELEBI, F. V., & Nassar, Y. F. (2026). A Novel Hybrid Optimization Framework for Renewable Energy Investment in Hot Arid Regions: Integrating Time-Series Forecasting and AI-Driven Algorithms for Algeria’s Grid-Export Strategy. Fezzan University Journal, 62-76.

[32] A-abdullatef, M. M., Osman, M. O. M., Elghaffi, F. S. A., Dalla, L. O. B., Agila, A. A. A., & Alsharif, A. (2025). LATENT: Low-Latency Anomaly Tracking in National Electricity Time-Series Using Hybrid LSTM-Regression Architectures–A Case Study of Bangladesh’s PGCB Grid. Comprehensive Science Journal, 9(36), 1891-1912