A deep learning and IoT-driven framework for real-time adaptive resource allocation and grid optimization in smart energy systems
Abstract The rapid evolution of smart grids, driven by rising global energy demand and renewable energy integration, calls for intelligent, adaptive, and energy-efficient resource allocation strategies. Traditional energy management methods, based on static models or heuristic algorithms, often fail...
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Nature Portfolio
2025-06-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-02649-w |
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| author | Arvind R. Singh M. S. Sujatha Akshay D. Kadu Mohit Bajaj Hailu Kendie Addis Kota Sarada |
| author_facet | Arvind R. Singh M. S. Sujatha Akshay D. Kadu Mohit Bajaj Hailu Kendie Addis Kota Sarada |
| author_sort | Arvind R. Singh |
| collection | DOAJ |
| description | Abstract The rapid evolution of smart grids, driven by rising global energy demand and renewable energy integration, calls for intelligent, adaptive, and energy-efficient resource allocation strategies. Traditional energy management methods, based on static models or heuristic algorithms, often fail to handle real-time grid dynamics, leading to suboptimal energy distribution, high operational costs, and significant energy wastage. To overcome these challenges, this paper presents ORA-DL (Optimized Resource Allocation using Deep Learning) an advanced framework that integrates deep learning, Internet of Things (IoT)-based sensing, and real-time adaptive control to optimize smart grid energy management. ORA-DL employs deep neural networks, reinforcement learning, and multi-agent decision-making to accurately predict energy demand, allocate resources efficiently, and enhance grid stability. The framework leverages both historical and real-time data for proactive power flow management, while IoT-enabled sensors ensure continuous monitoring and low-latency response through edge and cloud computing infrastructure. Experimental results validate the effectiveness of ORA-DL, achieving 93.38% energy demand prediction accuracy, improving grid stability to 96.25%, and reducing energy wastage to 12.96%. Furthermore, ORA-DL enhances resource distribution efficiency by 15.22% and reduces operational costs by 22.96%, significantly outperforming conventional techniques. These performance gains are driven by real-time analytics, predictive modelling, and adaptive resource modulation. By combining AI-driven decision-making, IoT sensing, and adaptive learning, ORA-DL establishes a scalable, resilient, and sustainable energy management solution. The framework also provides a foundation for future advancements, including integration with edge computing, cybersecurity measures, and reinforcement learning enhancements, marking a significant step forward in smart grid optimization. |
| format | Article |
| id | doaj-art-cf998b6e2fe74f1ba890365764ab9113 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-cf998b6e2fe74f1ba890365764ab91132025-08-20T03:10:35ZengNature PortfolioScientific Reports2045-23222025-06-0115112310.1038/s41598-025-02649-wA deep learning and IoT-driven framework for real-time adaptive resource allocation and grid optimization in smart energy systemsArvind R. Singh0M. S. Sujatha1Akshay D. Kadu2Mohit Bajaj3Hailu Kendie Addis4Kota Sarada5Department of Electrical Engineering, School of Physics and Electronic Engineering, Hanjiang Normal UniversityDepartment of EEE, School of Engineering, Mohan Babu UniversityDepartment of Electronics Engineering, Yeshwantrao Chavan College of EngineeringDepartment of Electrical Engineering, Graphic Era (Deemed to be University)Amhara Agricultural Research Institute, Soil and Water Management Research DirectorateDepartment of EEE, Koneru Lakshmaiah Education FoundationAbstract The rapid evolution of smart grids, driven by rising global energy demand and renewable energy integration, calls for intelligent, adaptive, and energy-efficient resource allocation strategies. Traditional energy management methods, based on static models or heuristic algorithms, often fail to handle real-time grid dynamics, leading to suboptimal energy distribution, high operational costs, and significant energy wastage. To overcome these challenges, this paper presents ORA-DL (Optimized Resource Allocation using Deep Learning) an advanced framework that integrates deep learning, Internet of Things (IoT)-based sensing, and real-time adaptive control to optimize smart grid energy management. ORA-DL employs deep neural networks, reinforcement learning, and multi-agent decision-making to accurately predict energy demand, allocate resources efficiently, and enhance grid stability. The framework leverages both historical and real-time data for proactive power flow management, while IoT-enabled sensors ensure continuous monitoring and low-latency response through edge and cloud computing infrastructure. Experimental results validate the effectiveness of ORA-DL, achieving 93.38% energy demand prediction accuracy, improving grid stability to 96.25%, and reducing energy wastage to 12.96%. Furthermore, ORA-DL enhances resource distribution efficiency by 15.22% and reduces operational costs by 22.96%, significantly outperforming conventional techniques. These performance gains are driven by real-time analytics, predictive modelling, and adaptive resource modulation. By combining AI-driven decision-making, IoT sensing, and adaptive learning, ORA-DL establishes a scalable, resilient, and sustainable energy management solution. The framework also provides a foundation for future advancements, including integration with edge computing, cybersecurity measures, and reinforcement learning enhancements, marking a significant step forward in smart grid optimization.https://doi.org/10.1038/s41598-025-02649-wAdaptive learningDeep learningGrid optimizationInternet of thingsMulti-agent systemsReal-time analytics |
| spellingShingle | Arvind R. Singh M. S. Sujatha Akshay D. Kadu Mohit Bajaj Hailu Kendie Addis Kota Sarada A deep learning and IoT-driven framework for real-time adaptive resource allocation and grid optimization in smart energy systems Scientific Reports Adaptive learning Deep learning Grid optimization Internet of things Multi-agent systems Real-time analytics |
| title | A deep learning and IoT-driven framework for real-time adaptive resource allocation and grid optimization in smart energy systems |
| title_full | A deep learning and IoT-driven framework for real-time adaptive resource allocation and grid optimization in smart energy systems |
| title_fullStr | A deep learning and IoT-driven framework for real-time adaptive resource allocation and grid optimization in smart energy systems |
| title_full_unstemmed | A deep learning and IoT-driven framework for real-time adaptive resource allocation and grid optimization in smart energy systems |
| title_short | A deep learning and IoT-driven framework for real-time adaptive resource allocation and grid optimization in smart energy systems |
| title_sort | deep learning and iot driven framework for real time adaptive resource allocation and grid optimization in smart energy systems |
| topic | Adaptive learning Deep learning Grid optimization Internet of things Multi-agent systems Real-time analytics |
| url | https://doi.org/10.1038/s41598-025-02649-w |
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