AI-Enabled Predictive Analytics for Sustainable Energy Consumption in Smart Cities
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.
Article Information
Journal |
International Journal of Science, Research and Technology |
|---|---|
Volume (Issue) |
Vol. 7 No. 6 (2024): International Journal of Science, Research and Technology (IJSRAT) |
DOI |
|
Pages |
15180-15194 |
Published |
December 9, 2024 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Mallesham Goli (%2024). AI-Enabled Predictive Analytics for Sustainable Energy Consumption in Smart Cities. International Journal of Science, Research and Technology , Vol. 7 No. 6 (2024): International Journal of Science, Research and Technology (IJSRAT) , pp. 15180-15194. https://doi.org/10.15662/IJSRAT.2024.0706006 |
References
1. Kong, W., Dong, Z. Y., Hill, D. J., Luo, F., & Xu, Y. (2017). Short-term residential load forecasting based on LSTM recurrent neural network. IEEE Transactions on Smart Grid, 10(1), 841–851.
2. Rongali, S. K. (2023). Explainable Artificial Intelligence (XAI) Framework for Transparent Clinical Decision Support Systems. International Journal of Medical Toxicology and Legal Medicine, 26(3), 22-31.
3. Mocanu, E., Nguyen, P. H., Gibescu, M., & Kling, W. L. (2016). Deep learning for estimating building energy consumption. Sustainable Energy, Grids and Networks, 6, 91–99.
4. Lahari Pandiri, "AI-Powered Fraud Detection Systems in Professional and Contractors Insurance Claims," International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering (IJIREEICE), DOI 10.17148/IJIREEICE.2024.121206.
5. Zhao, H., Guo, S., & Zhao, H. (2022). A review of energy forecasting models based on machine learning. Renewable and Sustainable Energy Reviews, 154, 111876.
6. Koppolu, H. K. R., & Sheelam, G. K. (2024). Machine Learning-Driven Optimization in 6G Telecommunications: The Role of Intelligent Wireless and Semiconductor Innovation. Global Research Development (GRD) ISSN: 2455-5703, 9(12).
7. Wang, H., Chen, Q., Kang, C., & Xia, Q. (2019). Clustering electricity consumption behavior in smart grids. Applied Energy, 234, 1085–1093.
8. Guntupalli, R. (2024). Enhancing Cloud Security with AI: A Deep Learning Approach to Identify and Prevent Cyberattacks in Multi-Tenant Environments. Available at SSRN 5329132.
9. Li, K., Hu, C., Liu, G., & Xue, W. (2020). Building electricity consumption prediction using optimized LSTM. Energy and Buildings, 204, 109500.
10. Pandugula, C., Kalisetty, S., & Polineni, T. N. S. (2024). Omni-channel Retail: Leveraging Machine Learning for Personalized Customer Experiences and Transaction Optimization. Utilitas Mathematica, 121, 389-401.
11. Siano, P. (2014). Demand response and smart grids. Renewable and Sustainable Energy Reviews, 30, 461–478.
12. Inala, R. AI-Powered Investment Decision Support Systems: Building Smart Data Products with Embedded Governance Controls.
13. Lund, H., Østergaard, P. A., Connolly, D., & Mathiesen, B. V. (2017). Smart energy systems for smart cities. Energy, 137, 556–565.
14. Uday Surendra Yandamuri. (2023). An Intelligent Analytics Framework Combining Big Data and Machine Learning for Business Forecasting. International Journal Of Finance, 36(6), 682-706. https://doi.org/10.5281/zenodo.18095256.
15. Mohsenian-Rad, A. H., Wong, V. W. S., Jatskevich, J., et al. (2010). Autonomous demand-side management. IEEE Transactions on Smart Grid, 1(3), 320–331.
16. Kolla, S. K. (2021). Architectural Frameworks for Large-Scale Electronic Health Record Data Platforms. Current Research in Public Health, 1(1), 1–19. Retrieved from https://www.scipublications.com/journal/index.php/crph/article/view/1372.
17. International Energy Agency. (2023). World energy outlook 2023. IEA Publications.
18. Guntupalli, R. (2024). AI-Powered Infrastructure Management in Cloud Computing: Automating Security Compliance and Performance Monitoring. Available at SSRN 5329147.
19. European Commission. (2020). A European green deal. Publications Office of the European Union.
20. Kalisetty, S. (2024). Deep learning frameworks for multi-modal data fusion in retail supply chains: enhancing forecast accuracy and agility.
21. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
22. Meda, R. (2024). Agentic AI in Multi-Tiered Paint Supply Chains: A Case Study on Efficiency and Responsiveness. Journal of Compu-tational Analysis and Applications (JoCAAA), 33(08), 3994-4015.
23. Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.
24. Vardhan Kumar Bandi, V. D. (2024). Automated Feature Engineering Systems in Large-Scale Healthcare Data Environments. Journal of Neonatal Surgery, 13(1), 2127–2141. Retrieved from https://www.jneonatalsurg.com/index.php/jns/article/view/10004.
25. Ke, G., Meng, Q., Finley, T., et al. (2017). LightGBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 30, 3146–3154.
26. Aitha, A. R. (2024). Generative AI-Powered Fraud Detection in Workers' Compensation: A DevOps-Based Multi-Cloud Architecture Leveraging, Deep Learning, and Explainable AI. Deep Learning, and Explainable AI (July 26, 2024).
27. Pinson, P. (2013). Wind energy: Forecasting challenges. Energy Policy, 56, 1–3.
28. Kalisetty, S., & Singireddy, J. (2023). Optimizing Tax Preparation and Filing Services: A Comparative Study of Traditional Methods and AI Augmented Tax Compliance Frameworks. Available at SSRN 5206185.
29. Nowotarski, J., & Weron, R. (2018). Recent advances in electricity price forecasting. International Journal of Forecasting, 34(4), 803–819.
30. Gottimukkala, V. R. R. (2023). Privacy-Preserving Machine Learning Models for Transaction Monitoring in Global Banking Networks. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 633-652.
31. Sun, Y., Wang, Z., & Li, H. (2024). Hybrid deep learning framework for multi-scale urban energy forecasting in smart grids. IEEE Transactions on Smart Grid, 15(2), 1654–1666.
32. Davuluri, P. N. Integrating Artificial Intelligence into Event-Driven Financial Crime Compliance Platforms.
33. Park, J., Kim, S., & Lee, J. (2024). Federated learning-enabled demand response optimization in smart city energy systems. IEEE Access, 12, 44521–44535.
34. Nagubandi, A. R. (2023). Advanced Multi-Agent AI Systems for Autonomous Reconciliation Across Enterprise Multi-Counterparty Derivatives, Collateral, and Accounting Platforms. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 653-674.
35. Zhang, C., Li, X., & Sun, H. (2023). Graph neural networks for spatiotemporal energy forecasting in smart cities. Energy, 263, 125734.
36. Kushvanth Chowdary Nagabhyru. (2023). Accelerating Digital Transformation with AI Driven Data Engineering: Industry Case Studies from Cloud and IoT Domains. Educational Administration: Theory and Practice, 29(4), 5898–5910. https://doi.org/10.53555/kuey.v29i4.10932.
37. Mnih, V., Kavukcuoglu, K., Silver, D., et al. (2015). Human-level control through deep reinforcement learning. Nature, 518, 529–533.
38. Agentic AI in Data Pipelines: Self Optimizing Systems for Continuous Data Quality, Performance and Governance. (2024). American Data Science Journal for Advanced Computations (ADSJAC) ISSN: 3067-4166, 2(1).
39. Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction (2nd ed.). MIT Press.
40. Garapati, R. S. (2023). Optimizing Energy Consumption in Smart Build-ings Through Web-Integrated AI and Cloud-Driven Control Systems.
41. Kalisetty, S. (2023). The Role of Circular Supply Chains in Achieving Sustainability Goals: A 2023 Perspective on Recycling, Reuse, and Resource Optimization. Reuse, and Resource Optimization (June 15, 2023).
42. Dwork, C., & Roth, A. (2014). The algorithmic foundations of differential privacy. Foundations and Trends in Theoretical Computer Science, 9(3–4), 211–407.
43. Nagabhyru, K. C. (2024). Data Engineering in the Age of Large Language Models: Transforming Data Access, Curation, and Enterprise Interpretation. Computer Fraud and Security.
44. Strbac, G. (2008). Demand side management: Benefits and challenges. Energy Policy, 36(12), 4419–4426.
45. Velangani Divya Vardhan Kumar Bandi. (2024). Intelligent Data Platforms For Personalized Retail Analytics At Scale. Metallurgical and Materials Engineering, 30(4), 1011–1027. Retrieved from https://metall-mater-eng.com/index.php/home/article/view/1011-1027.
46. Conejo, A. J., Morales, J. M., & Baringo, L. (2010). Real-time demand response model. IEEE Transactions on Smart Grid, 1(3), 236–242.
47. Keerthi Amistapuram. (2023). Privacy-Preserving Machine Learning Models for Sensitive Customer Data in Insurance Systems. Educational Administration: Theory and Practice, 29(4), 5950–5958. https://doi.org/10.53555/kuey.v29i4.10965.
48. Oldewurtel, F., Parisio, A., Jones, C. N., et al. (2012). Use of model predictive control in building climate control. Energy and Buildings, 45, 15–27.
49. Chava, K. (2024). The Role of Cloud Computing in Accelerating AI-Driven Innovations in Healthcare Systems. European Advanced Journal for Emerging Technologies (EAJET)-p-ISSN 3050-9734 en e-ISSN 3050-9742, 2(1).
50. Singireddy, J. (2024). AI-Enhanced Tax Preparation and Filing: Automating Complex Regulatory Compliance. European Data Science Journal (EDSJ) p-ISSN 3050-9572 en e-ISSN 3050-9580, 2(1).
51. Wei, T., Wang, Y., & Zhu, Q. (2017). Deep reinforcement learning for building HVAC control. DAC Proceedings.
52. Deep Learning-Driven Optimization of ISO 20022 Protocol Stacks for Secure Cross-Border Messaging. (2024). MSW Management Journal, 34(2), 1545-1554.
53. Dong, B., Cao, C., & Lee, S. E. (2005). Applying support vector machines to predict building energy consumption. Energy and Buildings, 37(5), 545–553.
54. Siva Hemanth Kolla. (2023). Deep Learning–Driven Retrieval-Augmented Generation for Enterprise ITSM Automation: A Governance-Aligned Large Language Model Architecture. Journal of Computational Analysis and Applications (JoCAAA), 31(4), 2489–2502. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/4774.
55. Maguluri, K. K., Pandugula, C., Kalisetty, S., & Mallesham, G. (2022). Advancing Pain Medicine with AI and Neural Networks: Predictive Analytics and Personalized Treatment Plans for Chronic and Acute Pain Managements. Journal of Artificial Intelligence and Big Data, 2(1), 112-126.
56. Hart, G. W. (1992). Nonintrusive appliance load monitoring. Proceedings of the IEEE, 80(12), 1870–1891.
57. Zoha, A., Gluhak, A., Imran, M. A., & Rajasegarar, S. (2012). Non-intrusive load monitoring approaches. Sensors, 12(12), 16838–16866.
58. Meda, R. (2023). Intelligent Infrastructure for Real-Time Inventory and Logistics in Retail Supply Chains. Educational Administration: Theory and Practice.
59. Ahmad, A., et al. (2023). Deep learning-based probabilistic load forecasting for smart cities. IEEE Access, 11, 98765–98778.
60. Davuluri, P. S. L. N. (2024). AI-Driven Data Governance Frameworks for Automated Regulatory Reporting and Audit Readiness. Metallurgical and Materials Engineering, 30(4), 996–1010. Retrieved from https://metall-mater-eng.com/index.php/home/article/view/1936.
61. Hippert, H. S., Pedreira, C. E., & Souza, R. C. (2001). Neural networks for short-term load forecasting. IEEE Transactions on Power Systems, 16(1), 44–55.
62. Rongali, S. K. (2024). Federated and Generative AI Models for Secure, Cross-Institutional Healthcare Data Interoperability. Journal of Neonatal Surgery, 13(1), 1683-1694.
63. Box, G. E. P., & Jenkins, G. M. (1976). Time series analysis: Forecasting and control. Holden-Day.
64. Segireddy, A. R. (2024). Machine Learning-Driven Anomaly Detection in CI/CD Pipelines for Financial Applications. Journal of Computational Analysis and Applications, 33(8).
65. Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2022). Statistical and ML forecasting methods: M5 competition results. International Journal of Forecasting, 38(4), 1346–1364.
66. Varri, D. B. S. (2024). Adaptive and Autonomous Security Frameworks Using Generative AI for Cloud Ecosystems. Available at SSRN 5774785.
67. IRENA. (2023). World energy transitions outlook 2023. International Renewable Energy Agency.
68. Keerthi Amistapuram. (2024). Federated Learning for Cross-Carrier Insurance Fraud Detection: Secure Multi-Institutional Collaboration. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 6727–6738. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/3934.
69. Clement-Nyns, K., Haesen, E., & Driesen, J. (2010). The impact of charging plug-in hybrid vehicles. IEEE Transactions on Power Systems, 25(1), 371–380.
70. Varri, D. B. S. (2023). Advanced Threat Intelligence Modeling for Proactive Cyber Defense Systems. Available at SSRN 5774926.
71. Lopes, J. A. P., Soares, F. J., & Almeida, P. M. (2011). Integration of electric vehicles in the grid. Proceedings of the IEEE, 99(1), 168–183.
72. Paleti, S. (2024). Transforming Financial Risk Management with AI and Data Engineering in the Modern Banking Sector. American Journal of Analytics and Artificial Intelligence (ajaai) with ISSN 3067-283X, 2(1).
73. Connolly, D., Lund, H., Mathiesen, B. V., & Leahy, M. (2010). A review of computer tools for renewable energy integration. Applied Energy, 87(4), 1059–1082.
74. Sheelam, G. K., & Koppolu, H. K. R. (2024). From Transistors to Intelligence: Semiconductor Architectures Empowering Agentic AI in 5G and Beyond. Journal of Computational Analy- sis and Applications(JoCAAA), 33(08), 4518-4537.
75. Olivares, D. E., Mehrizi-Sani, A., Etemadi, A. H., et al. (2014). Trends in microgrid control. IEEE Transactions on Smart Grid, 5(4), 1905–1919.
76. Aitha, A. R. (2023). CloudBased Micro services Architecture for Seamless Insurance Policy Administration. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 607-632.
77. Zhang, Y., et al. (2024). Bayesian probabilistic load forecasting for urban smart grids. Energy, 290, 130124.
78. Bandi, V. D. V. K. (2023). Production-Grade Machine Learning Pipelines For Healthcare Predictive Analytics. South Eastern European Journal of Public Health, 189–205. Retrieved from https://www.seejph.com/index.php/seejph/article/view/7057.
79. Wang, J., et al. (2023). Digital twin-driven energy optimization in smart districts. Energy and Buildings, 281, 112734.
80. Inala, R. Revolutionizing Customer Master Data in Insurance Technology Platforms: An AI and MDM Architecture Perspective.
81. Batty, M., et al. (2012). Smart cities of the future. European Physical Journal Special Topics, 214, 481–518.
82. Kolla, S. H. (2024). RETRIEVAL-AUGMENTED GENERATION WITH SMALL LLMS FOR KNOWLEDGE-DRIVEN DECISION AUTOMATION IN ENTERPRISE SERVICE PLATFORMS. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 15(3), 476–486. https://doi.org/10.61841/turcomat.v15i3.15497.
83. Bibri, S. E., & Krogstie, J. (2017). Smart sustainable cities of the future. Sustainable Cities and Society, 31, 183–212.
84. Varri, D. B. S. (2022). A Framework for Cloud-Integrated Database Hardening in Hybrid AWS-Azure Environments: Security Posture Automation Through Wiz-Driven Insights. International Journal of Scientific Research and Modern Technology, 1(12), 216-226.
85. Kitchin, R. (2014). The real-time city? Big data and smart urbanism. GeoJournal, 79, 1–14.
86. Amistapuram, K. (2024). Generative AI in Insurance: Automating Claims Documentation and Customer Communication. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 15(3), 461–475. https://doi.org/10.61841/turcomat.v15i3.15474.
87. Sun, H., et al. (2024). Multi-horizon probabilistic energy forecasting using transformers. Renewable Energy, 230, 119921.
88. Kolla, S. K. (2021). Designing Scalable Healthcare Data Pipelines for Multi-Hospital Networks. World Journal of Clinical Medicine Research, 1(1), 1–14. Retrieved from https://www.scipublications.com/journal/index.php/wjcmr/article/view/1376.
89. He, M., et al. (2023). Integrated DER optimization in urban smart grids. Applied Energy, 340, 120987.
90. Yandamuri, U. S. AI-Driven Decision Support Systems for Operational Optimization in Hospitality Technology.
91. Agrawal, R., & Srikant, R. (2000). Privacy-preserving data mining. ACM SIGMOD Record, 29(2), 439–450.
92. Rongali, S. K., & Kumar Kakarala, M. R. (2024). Existing challenges in ethical AI: Addressing algorithmic bias, transparency, accountability and regulatory compliance
2. Rongali, S. K. (2023). Explainable Artificial Intelligence (XAI) Framework for Transparent Clinical Decision Support Systems. International Journal of Medical Toxicology and Legal Medicine, 26(3), 22-31.
3. Mocanu, E., Nguyen, P. H., Gibescu, M., & Kling, W. L. (2016). Deep learning for estimating building energy consumption. Sustainable Energy, Grids and Networks, 6, 91–99.
4. Lahari Pandiri, "AI-Powered Fraud Detection Systems in Professional and Contractors Insurance Claims," International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering (IJIREEICE), DOI 10.17148/IJIREEICE.2024.121206.
5. Zhao, H., Guo, S., & Zhao, H. (2022). A review of energy forecasting models based on machine learning. Renewable and Sustainable Energy Reviews, 154, 111876.
6. Koppolu, H. K. R., & Sheelam, G. K. (2024). Machine Learning-Driven Optimization in 6G Telecommunications: The Role of Intelligent Wireless and Semiconductor Innovation. Global Research Development (GRD) ISSN: 2455-5703, 9(12).
7. Wang, H., Chen, Q., Kang, C., & Xia, Q. (2019). Clustering electricity consumption behavior in smart grids. Applied Energy, 234, 1085–1093.
8. Guntupalli, R. (2024). Enhancing Cloud Security with AI: A Deep Learning Approach to Identify and Prevent Cyberattacks in Multi-Tenant Environments. Available at SSRN 5329132.
9. Li, K., Hu, C., Liu, G., & Xue, W. (2020). Building electricity consumption prediction using optimized LSTM. Energy and Buildings, 204, 109500.
10. Pandugula, C., Kalisetty, S., & Polineni, T. N. S. (2024). Omni-channel Retail: Leveraging Machine Learning for Personalized Customer Experiences and Transaction Optimization. Utilitas Mathematica, 121, 389-401.
11. Siano, P. (2014). Demand response and smart grids. Renewable and Sustainable Energy Reviews, 30, 461–478.
12. Inala, R. AI-Powered Investment Decision Support Systems: Building Smart Data Products with Embedded Governance Controls.
13. Lund, H., Østergaard, P. A., Connolly, D., & Mathiesen, B. V. (2017). Smart energy systems for smart cities. Energy, 137, 556–565.
14. Uday Surendra Yandamuri. (2023). An Intelligent Analytics Framework Combining Big Data and Machine Learning for Business Forecasting. International Journal Of Finance, 36(6), 682-706. https://doi.org/10.5281/zenodo.18095256.
15. Mohsenian-Rad, A. H., Wong, V. W. S., Jatskevich, J., et al. (2010). Autonomous demand-side management. IEEE Transactions on Smart Grid, 1(3), 320–331.
16. Kolla, S. K. (2021). Architectural Frameworks for Large-Scale Electronic Health Record Data Platforms. Current Research in Public Health, 1(1), 1–19. Retrieved from https://www.scipublications.com/journal/index.php/crph/article/view/1372.
17. International Energy Agency. (2023). World energy outlook 2023. IEA Publications.
18. Guntupalli, R. (2024). AI-Powered Infrastructure Management in Cloud Computing: Automating Security Compliance and Performance Monitoring. Available at SSRN 5329147.
19. European Commission. (2020). A European green deal. Publications Office of the European Union.
20. Kalisetty, S. (2024). Deep learning frameworks for multi-modal data fusion in retail supply chains: enhancing forecast accuracy and agility.
21. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
22. Meda, R. (2024). Agentic AI in Multi-Tiered Paint Supply Chains: A Case Study on Efficiency and Responsiveness. Journal of Compu-tational Analysis and Applications (JoCAAA), 33(08), 3994-4015.
23. Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.
24. Vardhan Kumar Bandi, V. D. (2024). Automated Feature Engineering Systems in Large-Scale Healthcare Data Environments. Journal of Neonatal Surgery, 13(1), 2127–2141. Retrieved from https://www.jneonatalsurg.com/index.php/jns/article/view/10004.
25. Ke, G., Meng, Q., Finley, T., et al. (2017). LightGBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 30, 3146–3154.
26. Aitha, A. R. (2024). Generative AI-Powered Fraud Detection in Workers' Compensation: A DevOps-Based Multi-Cloud Architecture Leveraging, Deep Learning, and Explainable AI. Deep Learning, and Explainable AI (July 26, 2024).
27. Pinson, P. (2013). Wind energy: Forecasting challenges. Energy Policy, 56, 1–3.
28. Kalisetty, S., & Singireddy, J. (2023). Optimizing Tax Preparation and Filing Services: A Comparative Study of Traditional Methods and AI Augmented Tax Compliance Frameworks. Available at SSRN 5206185.
29. Nowotarski, J., & Weron, R. (2018). Recent advances in electricity price forecasting. International Journal of Forecasting, 34(4), 803–819.
30. Gottimukkala, V. R. R. (2023). Privacy-Preserving Machine Learning Models for Transaction Monitoring in Global Banking Networks. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 633-652.
31. Sun, Y., Wang, Z., & Li, H. (2024). Hybrid deep learning framework for multi-scale urban energy forecasting in smart grids. IEEE Transactions on Smart Grid, 15(2), 1654–1666.
32. Davuluri, P. N. Integrating Artificial Intelligence into Event-Driven Financial Crime Compliance Platforms.
33. Park, J., Kim, S., & Lee, J. (2024). Federated learning-enabled demand response optimization in smart city energy systems. IEEE Access, 12, 44521–44535.
34. Nagubandi, A. R. (2023). Advanced Multi-Agent AI Systems for Autonomous Reconciliation Across Enterprise Multi-Counterparty Derivatives, Collateral, and Accounting Platforms. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 653-674.
35. Zhang, C., Li, X., & Sun, H. (2023). Graph neural networks for spatiotemporal energy forecasting in smart cities. Energy, 263, 125734.
36. Kushvanth Chowdary Nagabhyru. (2023). Accelerating Digital Transformation with AI Driven Data Engineering: Industry Case Studies from Cloud and IoT Domains. Educational Administration: Theory and Practice, 29(4), 5898–5910. https://doi.org/10.53555/kuey.v29i4.10932.
37. Mnih, V., Kavukcuoglu, K., Silver, D., et al. (2015). Human-level control through deep reinforcement learning. Nature, 518, 529–533.
38. Agentic AI in Data Pipelines: Self Optimizing Systems for Continuous Data Quality, Performance and Governance. (2024). American Data Science Journal for Advanced Computations (ADSJAC) ISSN: 3067-4166, 2(1).
39. Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction (2nd ed.). MIT Press.
40. Garapati, R. S. (2023). Optimizing Energy Consumption in Smart Build-ings Through Web-Integrated AI and Cloud-Driven Control Systems.
41. Kalisetty, S. (2023). The Role of Circular Supply Chains in Achieving Sustainability Goals: A 2023 Perspective on Recycling, Reuse, and Resource Optimization. Reuse, and Resource Optimization (June 15, 2023).
42. Dwork, C., & Roth, A. (2014). The algorithmic foundations of differential privacy. Foundations and Trends in Theoretical Computer Science, 9(3–4), 211–407.
43. Nagabhyru, K. C. (2024). Data Engineering in the Age of Large Language Models: Transforming Data Access, Curation, and Enterprise Interpretation. Computer Fraud and Security.
44. Strbac, G. (2008). Demand side management: Benefits and challenges. Energy Policy, 36(12), 4419–4426.
45. Velangani Divya Vardhan Kumar Bandi. (2024). Intelligent Data Platforms For Personalized Retail Analytics At Scale. Metallurgical and Materials Engineering, 30(4), 1011–1027. Retrieved from https://metall-mater-eng.com/index.php/home/article/view/1011-1027.
46. Conejo, A. J., Morales, J. M., & Baringo, L. (2010). Real-time demand response model. IEEE Transactions on Smart Grid, 1(3), 236–242.
47. Keerthi Amistapuram. (2023). Privacy-Preserving Machine Learning Models for Sensitive Customer Data in Insurance Systems. Educational Administration: Theory and Practice, 29(4), 5950–5958. https://doi.org/10.53555/kuey.v29i4.10965.
48. Oldewurtel, F., Parisio, A., Jones, C. N., et al. (2012). Use of model predictive control in building climate control. Energy and Buildings, 45, 15–27.
49. Chava, K. (2024). The Role of Cloud Computing in Accelerating AI-Driven Innovations in Healthcare Systems. European Advanced Journal for Emerging Technologies (EAJET)-p-ISSN 3050-9734 en e-ISSN 3050-9742, 2(1).
50. Singireddy, J. (2024). AI-Enhanced Tax Preparation and Filing: Automating Complex Regulatory Compliance. European Data Science Journal (EDSJ) p-ISSN 3050-9572 en e-ISSN 3050-9580, 2(1).
51. Wei, T., Wang, Y., & Zhu, Q. (2017). Deep reinforcement learning for building HVAC control. DAC Proceedings.
52. Deep Learning-Driven Optimization of ISO 20022 Protocol Stacks for Secure Cross-Border Messaging. (2024). MSW Management Journal, 34(2), 1545-1554.
53. Dong, B., Cao, C., & Lee, S. E. (2005). Applying support vector machines to predict building energy consumption. Energy and Buildings, 37(5), 545–553.
54. Siva Hemanth Kolla. (2023). Deep Learning–Driven Retrieval-Augmented Generation for Enterprise ITSM Automation: A Governance-Aligned Large Language Model Architecture. Journal of Computational Analysis and Applications (JoCAAA), 31(4), 2489–2502. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/4774.
55. Maguluri, K. K., Pandugula, C., Kalisetty, S., & Mallesham, G. (2022). Advancing Pain Medicine with AI and Neural Networks: Predictive Analytics and Personalized Treatment Plans for Chronic and Acute Pain Managements. Journal of Artificial Intelligence and Big Data, 2(1), 112-126.
56. Hart, G. W. (1992). Nonintrusive appliance load monitoring. Proceedings of the IEEE, 80(12), 1870–1891.
57. Zoha, A., Gluhak, A., Imran, M. A., & Rajasegarar, S. (2012). Non-intrusive load monitoring approaches. Sensors, 12(12), 16838–16866.
58. Meda, R. (2023). Intelligent Infrastructure for Real-Time Inventory and Logistics in Retail Supply Chains. Educational Administration: Theory and Practice.
59. Ahmad, A., et al. (2023). Deep learning-based probabilistic load forecasting for smart cities. IEEE Access, 11, 98765–98778.
60. Davuluri, P. S. L. N. (2024). AI-Driven Data Governance Frameworks for Automated Regulatory Reporting and Audit Readiness. Metallurgical and Materials Engineering, 30(4), 996–1010. Retrieved from https://metall-mater-eng.com/index.php/home/article/view/1936.
61. Hippert, H. S., Pedreira, C. E., & Souza, R. C. (2001). Neural networks for short-term load forecasting. IEEE Transactions on Power Systems, 16(1), 44–55.
62. Rongali, S. K. (2024). Federated and Generative AI Models for Secure, Cross-Institutional Healthcare Data Interoperability. Journal of Neonatal Surgery, 13(1), 1683-1694.
63. Box, G. E. P., & Jenkins, G. M. (1976). Time series analysis: Forecasting and control. Holden-Day.
64. Segireddy, A. R. (2024). Machine Learning-Driven Anomaly Detection in CI/CD Pipelines for Financial Applications. Journal of Computational Analysis and Applications, 33(8).
65. Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2022). Statistical and ML forecasting methods: M5 competition results. International Journal of Forecasting, 38(4), 1346–1364.
66. Varri, D. B. S. (2024). Adaptive and Autonomous Security Frameworks Using Generative AI for Cloud Ecosystems. Available at SSRN 5774785.
67. IRENA. (2023). World energy transitions outlook 2023. International Renewable Energy Agency.
68. Keerthi Amistapuram. (2024). Federated Learning for Cross-Carrier Insurance Fraud Detection: Secure Multi-Institutional Collaboration. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 6727–6738. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/3934.
69. Clement-Nyns, K., Haesen, E., & Driesen, J. (2010). The impact of charging plug-in hybrid vehicles. IEEE Transactions on Power Systems, 25(1), 371–380.
70. Varri, D. B. S. (2023). Advanced Threat Intelligence Modeling for Proactive Cyber Defense Systems. Available at SSRN 5774926.
71. Lopes, J. A. P., Soares, F. J., & Almeida, P. M. (2011). Integration of electric vehicles in the grid. Proceedings of the IEEE, 99(1), 168–183.
72. Paleti, S. (2024). Transforming Financial Risk Management with AI and Data Engineering in the Modern Banking Sector. American Journal of Analytics and Artificial Intelligence (ajaai) with ISSN 3067-283X, 2(1).
73. Connolly, D., Lund, H., Mathiesen, B. V., & Leahy, M. (2010). A review of computer tools for renewable energy integration. Applied Energy, 87(4), 1059–1082.
74. Sheelam, G. K., & Koppolu, H. K. R. (2024). From Transistors to Intelligence: Semiconductor Architectures Empowering Agentic AI in 5G and Beyond. Journal of Computational Analy- sis and Applications(JoCAAA), 33(08), 4518-4537.
75. Olivares, D. E., Mehrizi-Sani, A., Etemadi, A. H., et al. (2014). Trends in microgrid control. IEEE Transactions on Smart Grid, 5(4), 1905–1919.
76. Aitha, A. R. (2023). CloudBased Micro services Architecture for Seamless Insurance Policy Administration. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 607-632.
77. Zhang, Y., et al. (2024). Bayesian probabilistic load forecasting for urban smart grids. Energy, 290, 130124.
78. Bandi, V. D. V. K. (2023). Production-Grade Machine Learning Pipelines For Healthcare Predictive Analytics. South Eastern European Journal of Public Health, 189–205. Retrieved from https://www.seejph.com/index.php/seejph/article/view/7057.
79. Wang, J., et al. (2023). Digital twin-driven energy optimization in smart districts. Energy and Buildings, 281, 112734.
80. Inala, R. Revolutionizing Customer Master Data in Insurance Technology Platforms: An AI and MDM Architecture Perspective.
81. Batty, M., et al. (2012). Smart cities of the future. European Physical Journal Special Topics, 214, 481–518.
82. Kolla, S. H. (2024). RETRIEVAL-AUGMENTED GENERATION WITH SMALL LLMS FOR KNOWLEDGE-DRIVEN DECISION AUTOMATION IN ENTERPRISE SERVICE PLATFORMS. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 15(3), 476–486. https://doi.org/10.61841/turcomat.v15i3.15497.
83. Bibri, S. E., & Krogstie, J. (2017). Smart sustainable cities of the future. Sustainable Cities and Society, 31, 183–212.
84. Varri, D. B. S. (2022). A Framework for Cloud-Integrated Database Hardening in Hybrid AWS-Azure Environments: Security Posture Automation Through Wiz-Driven Insights. International Journal of Scientific Research and Modern Technology, 1(12), 216-226.
85. Kitchin, R. (2014). The real-time city? Big data and smart urbanism. GeoJournal, 79, 1–14.
86. Amistapuram, K. (2024). Generative AI in Insurance: Automating Claims Documentation and Customer Communication. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 15(3), 461–475. https://doi.org/10.61841/turcomat.v15i3.15474.
87. Sun, H., et al. (2024). Multi-horizon probabilistic energy forecasting using transformers. Renewable Energy, 230, 119921.
88. Kolla, S. K. (2021). Designing Scalable Healthcare Data Pipelines for Multi-Hospital Networks. World Journal of Clinical Medicine Research, 1(1), 1–14. Retrieved from https://www.scipublications.com/journal/index.php/wjcmr/article/view/1376.
89. He, M., et al. (2023). Integrated DER optimization in urban smart grids. Applied Energy, 340, 120987.
90. Yandamuri, U. S. AI-Driven Decision Support Systems for Operational Optimization in Hospitality Technology.
91. Agrawal, R., & Srikant, R. (2000). Privacy-preserving data mining. ACM SIGMOD Record, 29(2), 439–450.
92. Rongali, S. K., & Kumar Kakarala, M. R. (2024). Existing challenges in ethical AI: Addressing algorithmic bias, transparency, accountability and regulatory compliance