Predictive Intelligence for Sustainable Urban Energy Management
Keywords:
Sustainable Energy Systems, Demand-Side Management (DSM), Demand Response (DR), Energy Efficiency Optimization, AI-Enabled Energy Analytics, Smart Grid Technologies, Intelligent Energy Management Systems, Predictive Load Forecasting, Sustainable Energy Consumption Models, Smart City Energy Platforms, Distributed Energy Resource Integration, Real-Time Energy Monitoring, Multi-Timescale Energy Operations, Renewable Energy Integration, Energy System Complexity Modeling, Artificial Intelligence in Power Systems, Urban Energy Optimization Frameworks, Grid Resilience and Stability, Data-Driven Energy Policy Support, Intelligent Demand-Side Analytics.Abstract
The challenge of satisfying ever-growing energy demand while respecting environmental constraints is critical for both human history and present circumstances. Indeed, energy systems have gradually transformed, becoming remarkably complex over the last century, in order to meet the developments of the associated end-use sectors and continue to provide energy services. In this context, the management and operation of the energy systems at different timescales represent increasingly difficult tasks — especially for large geographical areas — but are fundamental to ensuring that daily lives are not unduly affected by energy shortages.
Demand-side management and demand response have appeared on the energy scene and become increasingly relevant over the years in the quest for sustainable development. Indeed, energy efficiency improvements and the optimization of the energy use at the demand level have great potential for operating and managing service supply in a sustainable way. Although demand-side management, demand response, and energy efficiency have very different goals, characteristics, and symbolism, they share many common traits that have led to their convergence and the merging together of demand-side activities. For this reason, such activities can be managed from a common framework in order to provide effective artificial intelligence–enabled predictive analytics in support of sustainable energy consumption also for smart (or intelligent) cities.
References
1. Ahmad, T., Zhang, D., Huang, C., Zhang, H., Dai, N., Song, Y., & Chen, H. (2021). Artificial intelligence in sustainable energy industry: Status quo, challenges and opportunities. Journal of Cleaner Production, 289, 125834.
2. Alahi, M. E. E., & Sukkuea, A. (2021). Smart sensing and artificial intelligence for sustainable energy management in smart cities. Sustainable Energy Technologies and Assessments, 47, 101435.
3. Danghi, P. S., Maniraj, K., Jain, P., Adilakshmi, K., Garapati, R. S., & Jain, S. K. (2025). Artificial Intelligence Based Energy Optimization Framework for Wireless Sensor Networks. In 2025 IEEE 5th International Conference on ICT in Business Industry & Government (ICTBIG) (pp. 1–6). IEEE. 2025 IEEE 5th International Conference on ICT in Business Industry & Government (ICTBIG). https://doi.org/10.1109/ictbig68706.2025.1132386
4. Bedi, G., Venayagamoorthy, G. K., Singh, R., Brooks, R. R., & Wang, K. C. (2021). Review of Internet of Things (IoT) in electric power and energy systems. IEEE Internet of Things Journal, 8(11), 8473–8499.
5. Elahe, M. F., Jin, M., & Zeng, P. (2021). Review of load data analytics using deep learning in smart grids: Open load datasets, methodologies, and application challenges. International Journal of Energy Research, 45(10), 14274–14305.
6. Amistapuram, K., Pandiri, L., Raju, V. R., Paleti, S., Singireddy, S., & Sheelam, G. K. (2025). AI-Based Cloud Infrastructure and MLOps Frameworks for Scalable Data Engineering Across Banking and Insurance. In 2025 IEEE International Conference on Communication Networks and Computing (CNC) (pp. 186–192). IEEE. 2025 IEEE International Conference on Communication Networks and Computing (CNC). https://doi.org/10.1109/cnc68716.2025.11484532
7. Fatema, I., Kong, X., & Fang, G. (2021). Electricity demand and price forecasting model for sustainable smart grid using comprehensive long short-term memory. International Journal of Sustainable Engineering, 14(6), 1714–1732.
8. Haben, S., Arora, S., Giasemidis, G., Voss, M., & Greetham, D. V. (2021). Review of low voltage load forecasting: Methods, applications, and recommendations. Applied Energy, 304, 117798.
9. Sudhakar, A. V. V., Inala, R., Verma, A. K., Nag, K., Pandey, V., & Anand, P. S. (2025, September). Hybrid Rule-Based and Machine Learning Framework for Embedding Anti-Discrimination Law in Automated Decision Systems. In 2025 International Conference on Intelligent Communication Networks and Computational Techniques (ICICNCT) (pp. 1-6). IEEE.
10. Jha, N., Prashar, D., Rashid, M., Gupta, S. K., & Saket, R. K. (2021). Electricity load forecasting and feature extraction in smart grid using neural networks. Computers & Electrical Engineering, 95, 107479.
11. Rai, S., & De, M. (2021). Analysis of classical and machine learning based short-term and mid-term load forecasting for smart grid. International Journal of Sustainable Energy, 40(9), 821–839.
12. Uppal, M., Kumar, D., & Garg, V. K. (2021). Short term load forecasting through heat index biasing approach for smart grid sustainability. Sustainable Energy Technologies and Assessments, 46, 101637.
13. Kummari, D. N., Burugulla, J. K. R., Malempati, M., Amistapuram, K., Garapati, R. S., & Nagabhyru, K. C. (2025, December). Enhancing Audit Compliance and Operational Efficiency in Manufacturing and Commercial Insurance Through Agentic AI and Data Engineering Frameworks. In 2025 IEEE International Conference on Communication Networks and Computing (CNC) (pp. 714-720). IEEE.
14. Wang, Y., Wang, J., Chen, X., & Zeng, B. (2021). A novel hybrid load forecasting framework with intelligent feature engineering and optimization algorithm in smart grid. Applied Energy, 299, 117178.
15. Wen, L., Zhou, K., Yang, S., & Li, L. (2021). Compression of smart meter big data: A survey. Renewable and Sustainable Energy Reviews, 145, 111056.
16. Oladosu, S. A., Ige, A. B., Ike, C. C., Adepoju, P. A., Amoo, O. O., & Afolabi, A. I. (2023). AI-driven security for next-generation data centers: Conceptualizing autonomous threat detection and response in cloud-connected environments. GSC Adv Res Rev, 15(2), 162-172.
17. Vanting, N. B., Ma, Z., & Jørgensen, B. N. (2021). A scoping review of deep neural networks for electric load forecasting. Energy Informatics, 4(1), 49.
18. Kaur, D., Islam, S. N., Mahmud, M. A., Haque, M. E., & Dong, Z. Y. (2022). Deep learning approaches for energy forecasting in smart grid systems: A review. Electric Power Systems Research, 202, 107593.
19. Balamurugan, J., Bhuvaneswari, M., Aitha, A. R., Nagaraju, S., Viswanathan, R., & Dhasarathan, N. (2025, November). Explanatory Transformer-Based Sequential Recommendation: Assessing BERTRec with SASRec for Customer Behaviour Prediction. In 2025 International Conference on Emerging Engineering Technologies and Applications (IC-EETA) (pp. 470-475). IEEE.
20. Luo, X., Wang, J., Dooner, M., & Clarke, J. (2022). Overview of current development in electrical energy storage technologies and the application potential in power systems. Applied Energy, 322, 119544.
21. Zhou, Y., Wu, J., Song, G., & Long, C. (2022). Data-driven energy management for smart cities: A review. Renewable and Sustainable Energy Reviews, 162, 112436.
22. Zhang, Y., Wang, J., & Liu, X. (2022). Artificial intelligence for smart energy systems: A review. Renewable and Sustainable Energy Reviews, 154, 111822.
23. Balamurugan, J., Aitha, A. R., Kishore, D. S. C., Reenaraj, T., Babu, T. S., & Kumar, B. B. (2025, November). Adaptive AI-Driven Optimization in Smart Manufacturing: A Hybrid Neural-Network and Genetic-Algorithm Approach. In 2025 International Conference on Emerging Engineering Technologies and Applications (IC-EETA) (pp. 690-697). IEEE.
24. Khan, Z. A., Jayaweera, D., Alvarez-Alvarado, M., & Islam, S. (2022). Machine learning applications in smart grid: A comprehensive review. Energies, 15(3), 987.
25. Li, K., Zhang, Y., Wang, S., & Yang, J. (2022). Digital twin-driven smart energy management for sustainable cities. Energy Reports, 8, 10541–10556.
26. Ahmad, T., Chen, H., Guo, Y., & Wang, J. (2022). Artificial intelligence-based energy management systems: A review for smart grids and smart buildings. Renewable and Sustainable Energy Reviews, 159, 112214.
27. Mattaparthi, R. (2025). GenAI-Augmented Diagnostic Reasoning for Diesel Engine Fault Triage: A Large Language Model Framework for Technician Decision Support at Scale. Journal of Material Sciences & Manufacturing Research, 6(12), 1.
28. Khosravi, A., Machado, L., & Nunes, R. O. (2022). Time-series forecasting for sustainable energy systems: A review. Renewable and Sustainable Energy Reviews, 155, 111915.
29. Abdel-Basset, M., Mohamed, R., Sallam, K. M., & Chakrabortty, R. K. (2023). Energy-Net: A deep learning framework for short-term energy consumption forecasting in smart cities. Sustainable Cities and Society, 90, 104356.
30. Alhamid, M. F., Rawashdeh, M., & Alshurideh, M. (2023). Artificial intelligence for sustainable smart city energy management: A comprehensive review. Sustainable Energy Technologies and Assessments, 56, 103090.
31. Radha, S., Gottimukkala, V. R. R., Thottara, S., & Vandhana, K. (2025, October). Adaptive Video Streaming Over 5G Networks Using Deep Reinforcement Learning with Closed-Loop Feedback Mechanism for Bitrate Control. In 2025 International Conference on Communication, Computer, and Information Technology (IC3IT) (pp. 1-6). IEEE.
32. Khan, Z. A., Javaid, N., Ullah, I., & Mahmood, A. (2023). Machine learning techniques for intelligent energy management in smart grids: A review. Energies, 16(5), 2214.
33. Mathumitha, R., Rathika, P., & Manimala, K. (2024). Intelligent deep learning techniques for energy consumption forecasting in smart buildings: A review. Artificial Intelligence Review, 57, Article 35.
34. Huotari, M., Malhi, A., & Främling, K. (2024). Machine learning applications for smart building energy utilization: A survey. Archives of Computational Methods in Engineering, 31(4), 2537–2556.
35. Kolla, S. H., & Mangala, N. (2025). DESIGNING AUTONOMOUS LLM AGENT FRAMEWORKS USING GEN AI PIPELINES TO ENHANCE CUSTOMER SERVICE MANAGEMENT AND KNOWLEDGE WORKFLOWS. Lex Localis-Journal of Local Self-Government, 23 (S6), 9719–9733.
36. Le, T. T., Priya, J. C., Le, H. C., Le, N. V. L., Duong, M. T., & Cao, D. N. (2024). Harnessing artificial intelligence for data-driven energy predictive analytics: A systematic survey towards enhancing sustainability. International Journal of Renewable Energy Development, 13(2), 270–293.
37. Selvaraj, R., Kuthadi, V. M., & Baskar, S. (2023). Smart building energy management and monitoring system based on artificial intelligence in smart city. Sustainable Energy Technologies and Assessments, 56, 103090.
38. Zhao, X., & Zhang, Y. (2024). Integrated management of urban resources toward net-zero smart cities considering renewable energies uncertainty and modeling in digital twin. Sustainable Energy Technologies and Assessments, 64, 103656.
39. Amistapuram, K., Pandiri, L., Raju, V. R., Paleti, S., Singireddy, S., & Sheelam, G. K. (2025, December). AI-Based Cloud Infrastructure and MLOps Frameworks for Scalable Data Engineering Across Banking and Insurance. In 2025 IEEE International Conference on Communication Networks and Computing (CNC) (pp. 186-192). IEEE.
40. Javaid, A., Sajid, M., Uddin, E., Waqas, A., & Ayaz, Y. (2024). Sustainable urban energy solutions: Forecasting energy production for hybrid solar-wind systems. Energy Conversion and Management, 302, 118120.
41. Das, A., Kong, W., Leach, A., Mathur, S. K., Sen, R., & Yu, R. (2023). Long-term forecasting with TiDE: Time-series dense encoder. Transactions on Machine Learning Research.
42. Li, Y., Wang, H., Zhang, J., & Chen, X. (2023). Deep reinforcement learning for energy optimization in smart buildings. Applied Energy, 342, 121162.
43. Mangalampalli, B. M., & Kolla, S. K. (2025). Large Language Models for Automated Healthcare Data Dictionary Generation and Maintenance. Vascular and Endovascular Review, 8(20s), 363-375.
44. Chen, Z., Liu, Y., Wang, P., & Li, X. (2023). AI-enabled demand response optimization for urban smart grids. IEEE Transactions on Smart Grid, 14(5), 3874–3886.
45. Kumar, P., Singh, A., & Sharma, V. (2023). Digital twin-enabled predictive energy management for sustainable smart cities. Energy Reports, 9, 7260–7274.
46. Nigam, N., Sireesha, B., Ediga, P., Segireddy, A. R., & Bokde, S. (2025, December). Comparative Evaluation of Cloud Security Algorithms Using Multiple Classifiers with an Optimized Intrusion Detection System. In 2025 IEEE 5th International Conference on ICT in Business Industry & Government (ICTBIG) (pp. 1-6). IEEE.
47. Wang, X., Zhou, L., & Zhang, H. (2023). Intelligent forecasting of urban electricity demand using transformer neural networks. Energy, 275, 127473.
48. Li, J., Sun, Y., & Wu, C. (2024). Electricity consumption forecasting for sustainable smart cities using machine learning methods. Internet of Things, 27, 101322.
49. Kolla, T. (2025). Anomaly Detection Models for Outlier Provider Behavior in Cost and Treatment Patterns. Journal of Computational Analysis & Applications, 34(12), 1204.
50. Zhou, K., Yang, S., & Shen, C. (2024). A systematic review towards integrative energy management of smart grids and urban energy systems. Renewable and Sustainable Energy Reviews, 189, 114023.
51. Ahmed, S., Hassan, M., & Ibrahim, A. (2024). Deep learning-based optimization of energy utilization in IoT-enabled smart cities: A pathway to sustainable development. Energy Reports, 12, 2946–2957.
52. Lebcir, I., Mageswari, S. U., Bhosale, Y. H., Nagubandi, A. R., & Mahabooba, M. M. Agile Strategic Management in the Age of Disruption: Leveraging AI and Data Analytics for Competitive Advantage.
53. Sharma, R., Gupta, S., & Verma, P. (2024). Enhancing urban sustainability through AI-driven energy management: Predicting consumption patterns and minimizing carbon footprints. In Proceedings of the 2024 International Conference on Sustainability and Technological Advancements in Engineering Domain (SUSTAINED). IEEE.
54. Rahman, M. A., Islam, M. S., & Hossain, M. (2024). Data-driven approaches to achieve energy transition: Statistical analysis of demand-side management and smart decision making in urban energy hub networks. Sustainable Cities and Society, 101, 105150.
55. Nagubandi, A. R. (2025). FinTech and Financial Inclusion in Emerging Economies: An Empirical Assessment. Advances in Consumer Research.
56. Zhang, L., Chen, Y., & Wang, Q. (2025). Predictive intelligence for intelligent urban energy systems using hybrid artificial intelligence models. Applied Energy, 377, 124102.
57. Camacho, J. J., Aguirre, B., Ponce, P., Anthony, B., & Molina, A. (2024). Leveraging artificial intelligence to bolster the energy sector in smart cities: A literature review. Energies, 17(2), 353.
58. Inala, R., Kaulwar, P. K., Nagabhyru, K. C., Adusupalli, B., & Arun Raj, S. R. (2025, October). Leveraging IEC 61850 for Interoperable and Resilient Smart Grid Communication Architecture. In International Conference on Microelectronics, Electromagnetics and Telecommunication (pp. 549-566). Cham: Springer Nature Switzerland.
59. Mishra, P., & Singh, G. (2023). Energy management systems in sustainable smart cities based on the Internet of Energy: A technical review. Energies, 16(19), 6903.
60. Selvaraj, R., Kuthadi, V. M., & Baskar, S. (2023). Smart building energy management and monitoring system based on artificial intelligence in smart city. Sustainable Energy Technologies and Assessments, 56, 103090.
61. Pareyani, S., Goswami, S., Geetha, Y., Dimri, S. K., Niharika, D. S., & Amistapuram, K. (2025, December). Smart Resource Allocation in Wireless Sensor Networks Through AI Techniques. In 2025 IEEE 5th International Conference on ICT in Business Industry & Government (ICTBIG) (pp. 1-6). IEEE.
62. Chen, L., Li, X., & Zhu, J. (2024). Carbon peak control for achieving net-zero renewable-based smart cities: Digital twin modeling and simulation. Sustainable Energy Technologies and Assessments, 65, 103792.
63. Chen, W.-H., & You, F. (2024). Sustainable energy management and control for decarbonization of complex multi-zone buildings with renewable solar and geothermal energies using machine learning, robust optimization, and predictive control. Applied Energy, 372, 123802.
64. Mangala, N. (2025). Agentic Data Pipelines: Autonomous ELT Orchestration Using AI Agents on Microsoft Fabric and Databricks. International Journal of Computer Technology and Electronics Communication, 8(6), 11891-11907.
65. Li, J., Sun, Y., & Wu, C. (2024). Electricity consumption forecasting for sustainable smart cities using machine learning methods. Internet of Things, 27, 101322.
66. Zhou, K., Yang, S., and colleagues. (2024). A systematic review towards integrative energy management of smart grids and urban energy systems. Renewable and Sustainable Energy Reviews, 189, 114023.
67. Kolla, T. (2025). Generative AI for Intelligent Medical Coding and Healthcare Analytics. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 8(6), 13285-13299.
68. Ahmed, S., Hassan, M., & Ibrahim, A. (2024). Deep learning-based optimization of energy utilization in IoT-enabled smart cities: A pathway to sustainable development. Energy Reports, 12, 2946–2957.
69. Das, A., Kong, W., Leach, A., Mathur, S. K., Sen, R., & Yu, R. (2023). Long-term forecasting with TiDE: Time-series dense encoder. Transactions on Machine Learning Research.
70. Srikanth, T., Segireddy, A. R., & Elavarasi, S. A. (2025, October). STaSFormer-SGAD: Semantic Triplet-Aware Spatial Flow-Guided Spatio-Temporal Graph for Anomaly Detection in Surveillance Videos. In 2025 International Conference on Communication, Computer, and Information Technology (IC3IT) (pp. 1-7). IEEE.
71. Guo, Q., Peng, Y., & Luo, K. (2024). The impact of artificial intelligence on energy environmental performance: Empirical evidence from cities in China. Energy Economics, 136, 108136.
72. Gadi, A. L., Garapati, R. S., Inala, R., Singireddy, J., & Kapila, D. (2025, October). Robust Mutual Authentication for Distributed IoT Systems: Balancing Security and Efficiency. In International Conference on Microelectronics, Electromagnetics and Telecommunication (pp. 538-548). Cham: Springer Nature Switzerland.
73. Le, T. T., Priya, J. C., Le, H. C., Le, N. V. L., Duong, M. T., & Cao, D. N. (2024). Harnessing artificial intelligence for data-driven energy predictive analytics: A systematic survey towards enhancing sustainability. International Journal of Renewable Energy Development, 13(2), 270–293.
74. Kolla, S. H. (2025). Autonomous Agentic Frameworks for Enterprise Service Operations and Intelligent Process Automation. International Journal of Science, Research and Technology, 8(4), 14643-14655.
75. Zhao, X., & Zhang, Y. (2024). Integrated management of urban resources toward net-zero smart cities considering renewable energies uncertainty and modeling in digital twin. Sustainable Energy Technologies and Assessments, 64, 103656.
76. Bandi, V. D. V. K. (2024). Intelligent Data Platforms For Personalized Retail Analytics At Scale. Metallurgical and Materials Engineering, 30(4), 1011-1027.
77. Javaid, A., Sajid, M., Uddin, E., Waqas, A., & Ayaz, Y. (2024). Sustainable urban energy solutions: Forecasting energy production for hybrid solar-wind systems. Energy Conversion and Management, 302, 118120.
78. Wang, X., Zhou, L., & Zhang, H. (2023). Intelligent forecasting of urban electricity demand using transformer neural networks. Energy, 275, 127473.
79. Li, Y., Wang, H., Zhang, J., & Chen, X. (2023). Deep reinforcement learning for energy optimization in smart buildings. Applied Energy, 342, 121162.
80. Radhakrishnan, P., Manoranjithem, V., Garapati, R. S., Singh, S., & Praveen, R. V. S. (2025, November). Random Forest–XGBoost Hybrid Model for Early Detection of Breast Cancer in Medical Imaging Datasets. In 2025 IEEE 3rd Global Conference on Wireless Computing and Networking (GCWCN) (pp. 1-6). IEEE.
81. Chen, Z., Liu, Y., Wang, P., & Li, X. (2023). AI-enabled demand response optimization for urban smart grids. IEEE Transactions on Smart Grid, 14(5), 3874–3886.
82. Kumar, P., Singh, A., & Sharma, V. (2023). Digital twin-enabled predictive energy management for sustainable smart cities. Energy Reports, 9, 7260–7274.
83. Khan, Z. A., Javaid, N., Ullah, I., & Mahmood, A. (2023). Machine learning techniques for intelligent energy management in smart grids: A review. Energies, 16(5), 2214.
84. Reddy, V. A. R. (2025). Journal of Rare Cardiovascular Diseases. Health, 5(3), 402-422.
85. Zhang, Y., Wang, J., & Liu, X. (2022). Artificial intelligence for smart energy systems: A review. Renewable and Sustainable Energy Reviews, 154, 111822.
86. Ahmad, T., Chen, H., Guo, Y., & Wang, J. (2022). Artificial intelligence-based energy management systems: A review for smart grids and smart buildings. Renewable and Sustainable Energy Reviews, 159, 112214.
87. Abd El-Latif, E. I., & El-dosuky, M. (2025). Explainable energy consumption and speed prediction in sustainable cities using deep learning. Neural Computing and Applications, 37, 6233–6249.
88. Kolla, S. K., & Bandi, V. D. V. K. (2025). Autonomous Clinical Monitoring Platforms Using Reinforcement Learning and Deep Neural Networks. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 8(6), 13300-13313.
89. Byeon, H., AlGhamdi, A., Keshta, I., Soni, M., Mekhmonov, S., & Singh, G. (2025). Deep learning and smart energy-based lightweight urban power load forecasting model for sustainable urban growth. Frontiers in Sustainable Cities, 6, 1487109.
90. Salihu, S. A., Kumari, S., & Bandyopadhyay, A. (2025). An ensemble learning based energy forecasting model: A sustainable home energy management system for smart city. Procedia Computer Science, 258, 1316–1325.
91. Abd El-Latif, E. I., El-dosuky, M., & Hassan, A. (2025). Explainable artificial intelligence for sustainable urban energy prediction. Neural Computing and Applications, 37, 6233–6249.
92. Davuluri, P. N. AI-Augmented Sanctions Screening: Enhancing Accuracy and Latency in Real Time Compliance Systems.
93. Authors. (2025). Machine learning for sustainable urban energy planning: A comparative model analysis. Energies, 19(1), 176.
94. Authors. (2025). AI-driven predictive models for sustainability. Journal of Environmental Management, 373, 123472.
95. Mangalampalli, B. M., Kolla, S. K., Bandi, V. D. V. K., Yandamuri, U. S., & Rani, P. S. (2025). Designing Intelligent Healthcare Ecosystems through Adaptive Data Integration and Autonomous Learning Systems. Vascular and Endovascular Review, 8(20s), 330-347.
96. Camacho, J. J., Aguirre, B., Ponce, P., Anthony, B., & Molina, A. (2024). Leveraging artificial intelligence to bolster the energy sector in smart cities: A literature review. Energies, 17(2), 353.
97. Sharma, R., Gupta, S., & Verma, P. (2024). Enhancing urban sustainability through AI-driven energy management: Predicting consumption patterns and minimizing carbon footprints. In Proceedings of the 2024 International Conference on Sustainability and Technological Advancements in Engineering Domain (SUSTAINED). IEEE.
98. Mangala, N., & Kolla, S. H. (2025). Real-Time Feature Engineering for Streaming AI Workloads Using PySpark and Azure Event Hubs. International Journal of Multidisciplinary Research in Science, Engineering, Technology & Management, 1(6), 93-105.
99. Zheng, S., Liu, S., Zhang, Z., Gu, D., Xia, C., Pang, H., & Ampaw, E. M. (2024). TRIZ method for urban building energy optimization: GWO-SARIMA-LSTM forecasting model. arXiv.
100. Xu, H., Omitaomu, F., Sabri, S., Zlatanova, S., Li, X., & Song, Y. (2024). Leveraging generative AI for urban digital twins: A scoping review on the autonomous generation of urban data, scenarios, designs, and 3D city models for smart city advancement. arXiv.
101. Pasqualetto, A., Serafini, L., & Sprocatti, M. (2024). Artificial intelligence approaches for energy efficiency: A review. arXiv.
102. Davuluri, P. N. Integrating Artificial Intelligence into Event-Driven Financial Crime Compliance Platforms.
103. Chen, W.-H., & You, F. (2024). Sustainable energy management and control for decarbonization of complex multi-zone buildings using machine learning, robust optimization, and predictive control. Applied Energy, 372, 123802.
104. Zhao, X., & Zhang, Y. (2024). Integrated management of urban resources toward net-zero smart cities considering renewable energy uncertainty and digital twin modeling. Sustainable Energy Technologies and Assessments, 64, 103656.
105. Li, J., Sun, Y., & Wu, C. (2024). Electricity consumption forecasting for sustainable smart cities using machine learning methods. Internet of Things, 27, 101322.
106. Ahmed, S., Hassan, M., & Ibrahim, A. (2024). Deep learning-based optimization of energy utilization in IoT-enabled smart cities. Energy Reports, 12, 2946–2957.
107. Guo, Q., Peng, Y., & Luo, K. (2024). The impact of artificial intelligence on energy environmental performance: Empirical evidence from cities in China. Energy Economics, 136, 108136.
108. Bandi, V. D. V. K. AI-Based Anomaly Detection Frameworks in Distributed Enterprise Data Systems.
109. Le, T. T., Priya, J. C., Le, H. C., Le, N. V. L., Duong, M. T., & Cao, D. N. (2024). Harnessing artificial intelligence for data-driven energy predictive analytics: A systematic survey toward enhancing sustainability. International Journal of Renewable Energy Development, 13(2), 270–293.
110. Selvaraj, R., Kuthadi, V. M., & Baskar, S. (2023). Smart building energy management and monitoring system based on artificial intelligence in smart cities. Sustainable Energy Technologies and Assessments, 56, 103090.
111. Zhou, K., Yang, S., & colleagues. (2024). A systematic review toward integrative energy management of smart grids and urban energy systems. Renewable and Sustainable Energy Reviews, 189, 114023.
112. Khan, Z. A., Javaid, N., Ullah, I., & Mahmood, A. (2023). Machine learning techniques for intelligent energy management in smart grids: A review. Energies, 16(5), 2214.
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