Singh Arvind R, Sujatha M S, Kadu Akshay D, Bajaj Mohit, Addis Hailu Kendie, Sarada Kota
Department of Electrical Engineering, School of Physics and Electronic Engineering, Hanjiang Normal University, Shiyan, China.
Department of EEE, School of Engineering, Mohan Babu University, Tirupati, Andhra Pradesh, India.
Sci Rep. 2025 Jun 2;15(1):19309. doi: 10.1038/s41598-025-02649-w.
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.
在全球能源需求不断增长和可再生能源整合的推动下，智能电网迅速发展，这就需要智能、自适应且节能的资源分配策略。基于静态模型或启发式算法的传统能源管理方法，往往无法应对实时电网动态变化，导致能源分配欠佳、运营成本高昂以及大量能源浪费。为克服这些挑战，本文提出了ORA-DL（基于深度学习的优化资源分配），这是一个先进的框架，它集成了深度学习、基于物联网（IoT）的传感技术和实时自适应控制，以优化智能电网能源管理。ORA-DL采用深度神经网络、强化学习和多智能体决策来准确预测能源需求、高效分配资源并增强电网稳定性。该框架利用历史数据和实时数据进行主动潮流管理，而物联网传感器则通过边缘和云计算基础设施确保持续监测和低延迟响应。实验结果验证了ORA-DL的有效性，能源需求预测准确率达到93.38%，电网稳定性提高到96.25%，能源浪费减少到12.96%。此外，ORA-DL将资源分配效率提高了15.22%，运营成本降低了22.96%，显著优于传统技术。这些性能提升得益于实时分析、预测建模和自适应资源调制。通过结合人工智能驱动的决策、物联网传感和自适应学习，ORA-DL建立了一个可扩展、有弹性且可持续的能源管理解决方案。该框架还为未来的发展奠定了基础，包括与边缘计算的集成、网络安全措施以及强化学习增强，标志着智能电网优化向前迈出了重要一步。
