Yapay zekanın güç sistemlerinde uygulanmasına ilişkin güncel atılımlar ve eleştirel algılar: Sistematik bir inceleme

Öz

Yapay Zekâ (YZ) destekli çözümler, güç üretimi ve dağıtımından tüketime kadar güç sistemlerini hızla dönüştürüyor. Bu inceleme, özellikle yük tahmini, anomali tespiti, kestirimci bakım, enerji yönetimi, yenilenebilir enerji entegrasyonu ve siber güvenlik gibi temel alanları nasıl geliştirdiğine odaklanarak, en son gelişmelerin kapsamlı bir analizini sunmaktadır. Birincil amacımız, son yapay zekâ tabanlı teknikleri, gerçek dünyadaki uygulamalarını ve geleneksel yöntemlerle karşılaştırıldığında performanslarını sistematik olarak incelemek ve eleştirel bir şekilde değerlendirmektir. Benzersiz vaka çalışmalarının ayrıntılı bir incelemesi aracılığıyla, bu YZ tekniklerinin doğruluğu nasıl önemli ölçüde artırdığını ve sistem güvenilirliğini nasıl geliştirdiğini gösteriyoruz. Etkileyici bir ilerleme kaydedilmiş olsa da, özellikle veri toplama zorlukları ve gelişmiş YZ modellerinin doğasında bulunan karmaşıklık konusunda literatürde devam eden kritik boşlukları tespit ediyoruz. Bu incelemenin yeniliği, mevcut çığır açan gelişmeleri vurgularken aynı zamanda bu çözülmemiş zorluklara eleştirel bir bakış açısı sağlamasında yatmaktadır. Çeşitli çalışmalarda tespit edilen sınırlamaları sentezliyor ve daha verimli ve dayanıklı güç sistemleri geliştirmeye yönelik gelecekteki araştırmalara rehberlik edecek somut öneriler sunuyoruz.

Anahtar Kelimeler

• Yapay zekâ
• Güç
• Şebeke
• Enerji
• Algılama
• Siber güvenlik

Current breakthroughs and critical perceptions on the application of artificial intelligence in power systems: A systematic review

Öz

Artificial Intelligence (AI) driven solutions are rapidly transforming power systems, from power generation and distribution to consumption. This review provides a comprehensive analysis of the latest advancements, specifically focusing on how AI enhances key areas such as load forecasting, anomaly detection, predictive maintenance, energy management, renewable energy integration, and cybersecurity. Our primary aim is to systematically review and critically evaluate recent AI-based techniques, their real-world implementations, and their performance compared to traditional methods. Through a detailed examination of unique case studies, we demonstrate how these AI techniques significantly improve accuracy and enhance system reliability. While impressive progress has been made, we identify critical gaps that persist in the literature, particularly regarding the challenges of data collection and the inherent complexity of advanced AI models. This review's novelty lies in its dual focus: highlighting current breakthroughs while also providing a critical perspective on these unresolved challenges. We synthesize the limitations identified in various studies and offer concrete recommendations to guide future research toward developing more efficient and resilient power systems.

Anahtar Kelimeler

• Artificial Intelligence
• Power
• Grid
• Energy
• Detection
• Cybersecurity

Kaynakça

1. [1] Jiang Y, Li X, Luo H, Yin S, Kaynak O. “Quo vadis artificial intelligence?’’. Discover Artificial Intelligence, 2(4), 2022.
2. [2] Hassani H, Silva ES, Unger S, TajMazinani M, Mac Feely S. “Artificial intelligence (AI) or intelligence augmentation (IA): What Is the future?”. AI, 1(2), 143-155, 2020.
3. [3] Xu Y, Liu X, Cao X, Huang C, Liu E, Qian S, Liu X, Wu Y, Dong F, Qiu C, Qiu J, Hua K, Su W, Wu J, Xu H, Han Y, Fu C, Yin Z, Liu M, Roepman R, Dietmann S, Virta M, Kengara F, Zhang Z, Zhang L, Zhao T, Dai J, Yang J, Lan L, Luo M, Liu Z, An T, Zhang B, He X, Cong S, Liu X, Zhang W, Lewis JP, Tiedje JM, Wang Q, An Z, Wang F, Zhang L, Huang T, Lu C, Cai Z, Wang F, Zhang J. “Artificial intelligence: A powerful paradigm for scientific research”. The Innovation, 2(4), 100179, 2021.
4. [4] Nivedhaa NA. “A Comprehensive review of AI's dependence on data”. International Journal of Artificial Intelligence and Data Science, 1(1), 1-11, 2024.
5. [5] Sarker IH. “AI-based modeling: Techniques, applications and research issues towards automation, intelligent and smart systems”. SN Comput. Sci., 3,158, 2022.
6. [6] Rashidi HH, Tran NK, Betts EV, Howell LP, Green R. “Artificial intelligence and machine learning in pathology: The present landscape of supervised methods”. Academic Pathology, 6, 2019.
7. [7] Mahato R. Artificial Intelligence, what is It? Editors: Sundari SP, Chong ST, Prabu M. Outcomes of Best Practices in Classroom Research, 197-202, New Delhi, India, L’ Ordine Nuovo Publication, 2022.
8. [8] Prakash N, Atiq A, Shahid M, Rani J, Dikshit S. “Merging minds and machines: The role of advancing AI in robotics”. EAI Endorsed Transactions on Internet of Things, 10, 2023.

9. [9] Saleh Z. “Artificial Intelligence Definition, Ethics, and Standards”. Electronics and Communication Law, Standard and Practice. The British University in Egypt, 2019.
10. [10] Zhang C, Lu Y. “Study on artificial intelligence: The State of the art and future prospects”. Journal of Industrial Information Integration, 23, 100224, 2021.
11. [11] Balbaa M, Abdurashidova M. “The impact of artificial intelligence in decision making: A comprehensive review”. EPRA International Journal of Economics, Business and Management Studies, 11(2), 27-38, 2024.
12. [12] Chennupati A. “The evolution of AI: What does the future hold in the next two years”. World Journal of Advanced Engineering Technology and Sciences, 12(1), 22-28, 2024.
13. [13] Pothen A. Artificial Intelligence and its Increasing Importance. Editors: Karthikeyan J, Hie TS, Jin NY. Learning Outcomes of Classroom Research, 74-81, New Delhi, India, L’ Ordine Nuovo Publication, 2022.
14. [14] Younesi A. Shayeghi H, Wang Z, Siano P, Sani AM, Safari A. “Trends in modern power systems resilience: State-of-the-art review”. Renewable and Sustainable Energy Reviews, 162, 112397, 2022.
15. [15] Kuriakose E, Aziz A, Qays MO, Hossain ML, Hasan R, Sohan MR. “Resiliency measurement framework for distributed energy resource integrated distribution networks”. Int. Conf. on Advances in Computing, Communication, Electrical, and Smart Systems, Dhaka, Bangladesh, 8-9 March 2024.
16. [16] Zohuri B, Mossavar-Rahmani F, Behgounia F. “Global electricity demand and clean energy source growth scenario”. J. Energy Power Eng., 16, 156-165, 2022.
17. [17] Khan W, Ishrat M. Unveiling the depths of explainable AI: A comprehensive review. Editors: Sharma S, Prakash A, Sugumaran V. Technological Advancements in Data Processing for Next Generation Intelligent Systems, 78-106, Hershey, Pennsylvania, USA, IGI Global, 2024.
18. [18] Abdalla AN, Nazir MS, Tao H, Cao S, Ji R, Jiang M, Yao L. “Integration of energy storage system and renewable energy sources based on artificial intelligence: An overview”. Journal Energy Storage, 40, 102811, 2021.
19. [19] Rodrigues L, Faria D, Coelho F, Mello J, Saraiva JT, Villar J, Bessa RJ. “Analysis of flexibility-centric energy and cross-sector business models”. 19th Int. Conf. Eur. Energy Market, Lappeenranta, Finland, 6-8 June 2023.
20. [20] Lyu W, Liu J. “Artificial intelligence and emerging digital technologies in the energy sector”. Appl. Energy, 303, 117615, 2021.
21. [21] Liu Z, Sun Y, Xing C, Liu J, He Y, Zhou Y, Zhang G. “Artificial intelligence powered large-scale renewable integrations in multi-energy systems for carbon neutrality transition: Challenges and future perspectives”. Energy and AI, 10, 100195, 2022.
22. [22] Huawei Technologies. “Artificial Intelligence Technology”. Hangzhou, China. Springer, 2021.
23. [23] Arrieta AB, Díaz-Rodríguez N, Del SJ, Bennetot A, Tabik S, Barbado A, Garcia S, Gil-Lopez S, Molina D, Benjamins R, Chatila R, Herrera F. “Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI”. Information Fusion, 58, 82-115, 2020.
24. [24] Vasudevan K, Pub I. “Applications of artificial intelligence in power electronics and drives systems: A comprehensive review”. Journal of Power Electronics, 1(1), 1-14, 2023.
25. [25] Emmert-Streib F, Yli-Harja O, Dehmer M. “Artificial intelligence: A clarification of misconceptions, myths and desired status”. Frontiers in Artificial Intelligence, 3, 524339, 2020.
26. [26] Stock S, Babazadeh D, Becker C. “Applications of artificial intelligence in distribution power system operation”. IEEE Access, 9, 150098-150119, 2021.
27. [27] Pandey U, Pathak A, Kumar A, Mondal S. “Applications of artificial intelligence in power system operation, control and planning: A review”. Clean Energy, 7(6), 1199-1218, 2023.
28. [28] Barja-Martinez S, Aragüés-Peñalba M, Munné-Colladso I, Lloret-Gallego P, Bullich-Massagué E, Villafafila-Robles R. “Artificial intelligence techniques for enabling Big Data services in distribution networks: A review”. Renewable and Sustainable Energy Reviews, 150, 111459, 2021.
29. [29] Heymann F, Quest H, Lopez-Garcia T, Ballif C, Galus M. “Reviewing 40 years of artificial intelligence applied to power systems - A taxonomic perspective”. Energy and AI, 15, 100322, 2024.
30. [30] Hussain S, Alammari R, Iqbal A, Shikfa A. “Application of artificial intelligence in electrical power systems”. IEEE International Conference on Informatics, IoT, and Enabling Technologies, Doha, Qatar, 2-5 March 2020.
31. [31] Chinda FE. “The impact of artificial intelligence on engineering innovations”. Int. J. Appl. Res. Technol., 12(7), 22-28, 2023.
32. [32] Khaleel M, Ahmed AA, Alsharif A. “Artificial intelligence in engineering”. Brilliance, 3(1), 32-42, 2023.
33. [33] Ahmad N, Ghadi Y, Adnan M, Ali M. “Load forecasting techniques for power system: Research challenges and survey”. IEEE Access, 10, 71054-71090, 2022.
34. [34] Habbak H, Mahmoud M, Metwally K, Fouda MM, Ibrahem, MI. “Load forecasting techniques and their applications in smart grids”. Energies, 16(3), 1480, 2023.
35. [35] Patel R, Patel M, Patel R. “A review: Introduction and understanding of load forecasting”. Journal of Applied Science and Computations, 6, 1449-1457, 2019.
36. [36] Tang X, Dai Y, Wang T, Chen Y. “Short-term power load forecasting based on multi-layer bidirectional recurrent neural network”. IET Generation, Transmission & Distribution, 13(17), 3847-3854, 2019.
37. [37] Rafi SH, Al-Masood N, Deeba SR, Hossain E. “A short-term load forecasting method using integrated CNN and LSTM network”. IEEE Access, 9, 32436-32448, 2021.
38. [38] Yadav M, Jamil M, Rizwan M, Kapoor R. “Application of Fuzzy-RBF-CNN ensemble model for short-term load forecasting”. Journal of Electrical and Computer Engineering, 2023(1), 669796, 2023.
39. [39] Zaboli A, Tuyet-Doan VN, Kim YH, Hong J, Su W. “An LSTM-SAE-based behind-the-meter load forecasting method”. IEEE Access, 11, 49378- 49392, 2023.
40. [40] Bracale A, Caramia P, De Falco P, Hong T. “A multivariate approach to probabilistic industrial load forecasting”. Electric Power Systems Research, 187, 106430, 2020.
41. [41] Himeur Y, Ghanem K, Alsalemi A, Bensaali F, Amira A. “Artificial intelligence-based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives”. Applied Energy, 287, 116601, 2021.
42. [42] Zhao Y, Zhang C, Zhang Y, Wang Z, Li J. “A review of data mining technologies in building energy systems: Load prediction, pattern identification, fault detection and diagnosis”. Energy and Built Environment, 1(2), 149-164, 2019.
43. [43] Kardi M, AlSkaif T, Tekinerdogan B, CatalãoJ PS. “Anomaly detection in electricity consumption data using deep learning”. Int. Conf. on Environment and Electrical Engineering and 2021 IEEE Industrial and Commercial Power Systems Europe, Bari, Italy, 7-10 September 2021.
44. [44] Park CH, Kim T. “Energy theft detection in advanced metering infrastructure based on anomaly pattern detection”. Energies, 13(15), 3832, 2020.
45. [45] Feng L, Xu S, Zhang L, Wu J, Zhang J, Chu C, Wang Z, Shi H. “Anomaly detection for electricity consumption in cloud computing: framework, methods, applications, and challenges”. EURASIP Journal on Wireless Communications and Networking, 2020, 194, 2020.
46. [46] Da Silva A, Guarany IS, Arruda B, Gurjao EC, Freire RS. “A method for anomaly prediction in power consumption using Long Short-Term Memory and Negative Selection”. IEEE International Symposium on Circuits and Systems, Sapporo, Japan, 26-29 May 2019.
47. [47] Zhang J, Zhang H, Ding, S, Zhang X. “Power consumption predicting and anomaly detection based on Transformer and K-Means”. Frontiers in Energy Research, 9, 2021.
48. [48] Erika S, Antonello R, Gianfraco DL, Massimo P, Rondolfo A. “Systematic review of energy theft practices and autonomous detection through artificial intelligence methods”. Renewable and Sustainable Energy Reviews 184, 11354. 2023.
49. [49] Mascali L, Schiera DS, Eiraudo S, Barbierato L, Giannantonio R, Patti E, Bottaccioli L, Lanzini A. “A machine learning-based anomaly detection framework for building electricity consumption data”. Sustain. Energy Grids Netw., 36, 101194, 2023.
50. [50] Wan Q, Li Y, Yuan R, Meng Q, Li X. “Fault identification and localization of a time-frequency domain joint impedance spectrum of cables based on deep belief Networks”. Sensors, 23(2), 684, 2023.
51. [51] Jaber AS, Kunna MA, Khatir SK. “Power systems optimization challenges and applications: An overview”. International Conference on the Cognitive Computing and Complex Data, Huaian, China, 21 - 22 October 2023.
52. [52] Dulau LI, Bica D. “Optimization of the reliability of power systems through allocation”. 56th International Universities Power Engineering Conference, Middlesbrough, United Kingdom, 31 August-3 September 2021.
53. [53] López E, Domínguez-Cruz RF. Salgado-Tránsito I. “Optimization of power generation grids: A case of study in Eastern Mexico”. Mathematics and Computer Applications, 26(2), 46, 2021.
54. [54] Pawar SS, Ingole A, Kadam P, Kadam YV, Atre P, Dhanke J. “Power transmission performance optimization: Intelligent approach needed”. European Chemical Bulletin, 12(4), 10862-10866, 2023.
55. [55] Fan H, Yu K, Li Z, Shahidehpour M. “Optimization of power supply capacity of distribution network considering the participation of power sales companies in spot power trading”. IEEE Access, 7, 99651-99657, 2019.
56. [56] Airoboman AE, EA Ogujor. “Reliability optimization on power systems network using genetic algorithm”. Journal of Electrical, Control and Telecommunication Research, 2, 18-29, 2020.
57. [57] Aneke NE, Ngang NB. “Reduction of power system losses in transmission lines using optimization technique”. International Journal of Engineering Science, 10(4), 58-66, 2021.
58. [58] Kurukuru VSB, Haque A, Khan MA, Sahoo S, Malik A, Blaabjerg F. “A review on artificial intelligence applications for grid-connected solar photovoltaic systems”. Energies, 14(15), 4690, 2021.
59. [59] Rahman J, Feng C, Zhang J. “A learning-augmented approach for AC optimal power flow”. International Journal of Electrical Power & Energy Systems, 130, 106908, 2021.
60. [60] Le TN, Nguyen HMV, Nguyen TA, Phung TT, Phan BD. “Optimization of load ranking and load shedding in a power system using the improved AHP algorithm”. Engineering, Technology & Applied Science Research, 12(3), 8512-8519, 2022.
61. [61] Le THN, Phung TT, Le TN, Nguyen PN. “Load shedding technique for power system using neural network improved by cuckoo search algorithm”. Journal of Technical Education Science, 78A, 63-72, 2023.
62. [62] Le TN, Nguyen HMV, Hoang TT, Nguyen NA. “Optimizing the power system operation problem towards minimizing generation and damage costs due to load shedding”. Engineering, Technology & Applied Science Research, 13(5), 11643-11648, 2023.
63. [63] Jasim AM, Jasim BH, Neagu B, Alhasnawi BN. “Efficient Optimization Algorithm-Based Demand-Side Management Program for Smart Grid Residential Load”. Axioms, 12(1), 33, 2022.
64. [64] Jamarani, A., Haddadi, S., Sarvizadeh, R., Haghi Kashani, M., Akbari, M., Moradi, S.’Big Data and Predictive analytics: A systematic review of applications’’. Artificial Intelligence Review 57, 176. https://doi.org/10.1007/s10462-024-10811-5. 2024
65. [65] Geng S, Wang X. “Predictive maintenance scheduling for multiple power equipment based on data-driven fault prediction”. Computers & Industrial Engineering, 164, 107898, 2021.
66. [66] Kane AP, Kore AS, Khandale AN, Nigade SS, Joshi PP. “Predictive maintenance using machine learning”. arXiv, May 2022. https://arxiv.org/pdf/2205.09402
67. [67] Zonta T, Da Costa CA, Zeiser FA, De Oliveira Ramos G, Kunst R, Da Rosa Righi R. “A predictive maintenance model for optimizing production schedule using deep neural networks”. Journal of Manufacturing Systems, 62, 450-462, 2022.
68. [68] Basit J, Zeb A. “Predictive maintenance using deep learning: Enhancing reliability and reducing electrical system downtime”. International Journal of Innovations in Science & Technology, 6(3), 1120-1136, 2024.
69. [69] Arez D, Navas HVG, Gaspar P. The Bridge between Artificial Intelligence and Predictive Maintenance in Industry 4.0: A Systematic Review. Applied Sciences. 2026;16(10):4882.https://doi.org/10.3390/app16104882
70. [70] Lekidis A, Georgakis A, Dalamagkas C, Papageorgiou E. “Predictive maintenance framework for fault detection in remote terminal units”. Forecasting, 6(2), 239-265, 2024.
71. [71] Alves F, Badikyan H, António Moreira HJ, Azevedo J, Moreira PM, Romero L, Leitão P. “Deployment of a smart and predictive maintenance system in an industrial case study”. IEEE international Symposium on Industrial Electronics, Delft, Netherlands, 17-19 June 2020.
72. [72] Onwusinkwue S, Osasona F, Ahmad I, Anyanwu A, Dawodu S, Obi O, Hamdan A. “Artificial intelligence (AI) in renewable energy: A review of predictive maintenance and energy optimization”. World J. Adv. Res. Rev., 21(1), 2487-2799, 2024.
73. [73] Yousuf H, Zainal AY, Alshurideh M, Salloum SA. Artificial Intelligence Models in Power System Analysis. Editors: Hassanien A, Bhatnagar R, Darwish A. Artificial Intelligence for Sustainable Development: Theory, Practice and Future Applications, 231-242. Cham, Switzerland, Springer, 2021.
74. [74] Laayati O, Amaziane O, Bouzi M, El Bazi N, El Hadraoui H, Chebak A. “Smart energy management system: Predictive maintenance for dry power transformers using transfer learning”. International Conference on Electrical, Computer, Communications and Mechatronics Engineering, Tenerife, Canary Islands, Spain, 19-21 July 2023.
75. [75] Laayati O, El Hadraoui H, Maghraoui A, El Bazi N, Bouzi M, Chebak A, Guerrero J. “An AI-layered with multi-agent systems architecture for prognostics health management of smart transformers: A novel approach for smart grid-ready energy management systems”. Energies, 15(19), 7217, 2022.
76. [76] Panmala N, Suwanasri T, Suwanasri C. “A fuzzy logic approach to health index determination for a gas-insulated switchgear”. Energies, 16(18), 6605, 2023.
77. [77] Phillip M, Singh A, Ramlal CJ. “Narrow band frequency response analysis of power transformers with deep learning”. Energies, 16(17), 6347, 2023.
78. [78] Li Y, Li Y, Wang Q, Wang W, Liu G, Li Z, Wu H, Jiang S. “Research on defect recognition technology of transmission line based on visual macromodeling”. Appl. Math. Nonlinear Sci., 9(1), 1-15, 2024.
79. [79] Heydari M, Heydari A, Amini M. “Energy management and energy consumption: A comprehensive study”. World Information Technology and Engineering Journal, 10(4), 22-28, 2023.
80. [80] Zhang J. “Energy management system: The engine for sustainable development and resource optimization”. Highlights in Science, Engineering and Technology, 76 (2023), 618-624, 2023.
81. [81] Akdağ O. “Intermittent earth fault function in electric distribution systems: significance, working principle, and application potential “. Pamukkale University Journal of Engineering Sciences, 31(1), 67-72, 2025.
82. [82] Cooremans C, Schoenenberger A. “Energy management: A key driver of energy-efficiency investment?”. Journal of Cleaner Production, 230, 264-275, 2019.
83. [83] Meliani M, El Barkany A, El Abbassi E, Darcherif AM, Mahmoudi M. “Energy management in the smart grid: State-of-the-art and future trends”. International Journal of Engineering Business Management, 13, 1-26, 2021.
84. [84] Alam MS, Arefifar S. “Energy management in power distribution systems: Review, classification, limitations and challenges”. IEEE Access, 7, 92979-93001, 2019.
85. [85] Kanungo SK. “Future trends in energy management systems: Opportunities and predictions for energy efficiency”. [Unpublished manuscript], 2024.
86. [86] Hanafi A, Moawed MA, Abdullatif OE. “Advancing sustainable energy management: A comprehensive review of artificial intelligence techniques in building”. Engineering Research Journal, 53(2), 26-46, 2024.
87. [87] Prakash KP, Kumar YVP, Ravindranath K, Reddy GP, Amir M, Khan B. “Artificial neural network-based data imputation for handling anomalous energy consumption readings in smart homes”. Energy Exploration & Exploitation, 42(4), 1432-1449, 2024.
88. [88] Obulareddy S, Ameeruddin RK. “ANFIS-based energy management system for microgrids”. Journal of Electrical Systems, 20(3), 1673-1682, 2024.
89. [89] Shaban A, Maher H, Elbayoumi M, Abdelhady S. “A cuckoo load scheduling optimization approach for smart energy management”. Energy Reports, 7, 4705-4721, 2021.
90. [90] Chojecki A, Rodak M, Ambroziak A, Borkowski P. “Energy management system for residential buildings based on fuzzy logic: Design and implementation in smart meter”. IET Smart Grid, 3(2), 254-266, 2020.
91. [91] Busisiwe RL, Mbuyu S, Reginald NN. “A fuzzy logic based residential electrical energy optimization system based on time of use tariffs”. International Energy Journal, 21(3), 415-426, 2024.
92. [92] Nazarov A, Sulimin V, Shvedov V. “Renewable energy sources: global implementation experience”. E3S Web of Conferences, 474, 01030, 2024.
93. [93] Ilojianya V, Umoh AA, Etukudoh EA, Ibekwe K, Hamdan A. “Renewable energy technologies in engineering: A review of current developments and future prospects”. International Journal of Science and Research Archive, 11(1), 952-964, 2024.
94. [94] Çapraz O. “Evaluation of renewable energy sources for Denizli using multi-criteria decision-making methods”. Pamukkale University Journal of Engineering Sciences, 31(2), 180-198, 2025.
95. [95] Umeh CA, Nwankwo AU, Oluka PO, Umeh CL, Ogbonnaya AL. “The role of renewable energies for sustainable energy governance and environmental policies for the mitigation of climate change in Nigeria”. European Journal of Applied Science, Engineering and Technology, 2(2), 71-98, 2024.
96. [96] Benchikh S, Tarik J, Lamrani R. “Enhancing renewable energy systems with advanced artificial intelligence solutions”. SCT Proceedings in Interdisciplinary Insights and Innovations, 2, 319, 2024.
97. [97] Nassereddine M, Nassereddine G, Arid A, Samad M. Artificial Intelligence Applications in Renewable Power Systems. Editors: Samad ME, Nassreddine G, El-chaarani H, Nemar SE. Industrial Applications of Big Data, AI, and Blockchain, 26-57, Hershey, USA, IGI Global, 2024.
98. [98] Ukoba K, Olatunji KO, Adeoye E, Jen TC, Madyira D. “Optimizing renewable energy systems through artificial intelligence: Review and future prospects”. Energy & Environment, 35(7), 3833-3879, 2024.
99. [99] Karmich S, Ziani EM, Jamal B, El Malki M. “Impact of integration of renewable energies and energy efficiency on the reliability of the national electricity grid”. International Journal of Power Electronics and Drive Systems, 14(4), 2433-2446, 2023.
100. [100] Rakib MW, Munna AH, Farooq T, Agili B, He M. “Enhancing grid stability and sustainability: Energy-storage-based hybrid systems for seamless renewable integration”. European Journal of Electrical Engineering and Computer Science, 8(3), 1-8, 2024.
101. [101] Maleki A, Nazari MA, Pourfayaz F. “Harmony search optimization for optimum sizing of hybrid solar schemes based on battery storage unit”. Energy Reports, 6(7), 102-111, 2020.
102. [102] Emam MM, El-Sattar HA, Houssein EH, Kamel S. “Modified orca predation algorithm: Developments and perspectives on global optimization and hybrid energy systems”. Neural Computing and Applications, 35, 15051-15073, 2023.
103. [103] Yekini SM, Mathurine G, Onyegbadue IA, Funsho O. “Techno-economic optimization of clean energy hybrid systems in the context of assorted battery storage technologies”. African Journal of Environmental Sciences and Renewable Energy, 15(1), 170-189, 2024.
104. [104] Duan F, Eslami M, Khajehzadeh M, Basem A, Jasim D, Palani S. “Optimization of a photovoltaic/wind/battery energy-based microgrid in distribution network using machine learning and fuzzy multi-objective improved Kepler optimizer algorithms”. Scientific Reports, 14, 13354, 2024.
105. [105] Alsuwian T, Shahid A, Amin AA. “Smart Grid Cyber Security Enhancement: Challenges and Solutions-A Review”. Sustainability, 14(21), 14226, 2022.
106. [106] Tatipatri N, Arun SL. “A comprehensive review on cyber-attacks in power systems: Impact analysis, detection, and cyber security”. IEEE Access, 12, 18147-18167, 2024.
107. [107] Ezurike BO, Nmadu D, Ajah SA, Uma UU, Nwawelu UN, Ogah OE, Osuji CC, Emeh JC, Okoronkwo CA. “Recent advances on the challenges of resilient automation systems of the power grid: Critical review”. Smart Grids and Sustainable Energy, 9, 42, 2024.
108. [108] Meduri K, Nadella G, Gonaygunta H. “Enhancing cybersecurity with artificial intelligence: Predictive techniques and challenges in the age of IoT”. International Journal of Science and Engineering Applications, 13(4), 30-33, 2024.
109. [109] Simoes MG, Elmusrati M, Vartiainen T, Mekkanen M, Karimi M, Diaba S, Anti E, Lopes W. “Enhancing data security against cyberattacks in artificial intelligence-based smart grid systems with crypto agility”. arXiv, 2023. https://arxiv.org/abs/2305.11652
110. [110] Mohamed N, Oubelaid A, Almazrouei S. “Staying ahead of threats: A review of AI and cyber security in power generation and distribution”. International Journal of Electrical and Electronics Research, 11(1), 143-147, 2023.
111. [111] Karimipour H, Dehghantanha A, Parizi R, Choo KR, Leung, H. “A deep and scalable unsupervised machine learning system for cyber-attack detection in large-scale smart grids”. IEEE Access, 7, 80778-80788, 2019.
112. [112] Camana MR, Ahmed S, García CE, Koo I. “Extremely randomized trees-based scheme for stealthy cyber-attack detection in smart grid networks”. IEEE Access, 8, 19921-19933, 2020.
113. [113] Li Y, Wei X, Li Y, Dong ZY, Shahidehpour M. “Detection of false data injection attacks in smart grid: A secure federated deep learning approach”. IEEE Transactions on Smart Grid, 13(6), 4862-4872, 2022.
114. [114] Naeem H, Ullah F, Srivastava G. “Classification of intrusion cyber-attacks in smart power grids using deep ensemble learning with metaheuristic-based optimization”. Expert Systems, 42(1), e13556, 2024.
115. [115] Harrou F, Bouyeddou B, Dairi A, Sun Y. “Exploiting autoencoder-based anomaly detection to enhance cybersecurity in power grids”. Future Internet, 16(6), 184, 2024.
116. [116] Ragula ST, Srinivas P, “Predictive battery health management using deep learning in a cloud-integrated web framework”. American Journal of AI Cyber Computing Management, 5(4), 403-409, 2025.
117. [117] Jo S, Jung S, Roh T. “Battery state-of-health estimation using machine learning and preprocessing with relative state-of-charge”. Energies, 14(21), 7206, 2021.
118. [118] Zhang S, Liu Z, Xu Y, Guo J, Su H. “A physics-informed hybrid data-driven approach with generative electrode-level features for lithium-ion battery health prognostics”. IEEE Transactions on Transportation Electrification, 11(1), 4857-4871, 2025.
119. [119] Iturrino Garcia CA, Bindi M, Corti F, Luchetta A, Grasso F, Paolucci L, Piccirilli MC, Aizenberg I. “Power quality analysis based on machine learning methods for low-voltage electrical distribution lines”. Energies, 16(9), 3627, 2023.
120. [120] Chinthaginjala R, Srinivasulu A, Agrawal A, Kim TH, Tera SP, Ahmad S. “Hybrid AI and semiconductor approaches for power quality improvement”. Scientific Reports, 15, 25640, 2025.
121. [121] Almalki AJ. “Unsupervised learning with hybrid models for detecting electricity theft in smart grids”. IEEE Access, 12, 187027-187040, 2024.