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<p>An emerging innovation in modern electrical power systems is the smart grid, which integrates advanced communication and control technologies to enhance the reliability and efficiency of traditional energy networks. To achieve optimal performance, continuous monitoring and intelligent control are essential. This research focuses on precise power generation tracking in smart grid environments using Wireless Sensor Networks (WSNs). A connection is established between power generation units and specialized sensor nodes to enable seamless and real-time data exchange. To address challenges related to energy efficiency and scalability, an enhanced Cluster Head (CH) selection strategy based on the Gazelle Optimization Algorithm (GOA) is proposed. Furthermore, a Deep Belief Network (DBN) is employed to ensure intelligent and efficient data transmission by minimizing routing distance and improving network lifetime. The proposed framework demonstrates significant improvements in data throughput, packet integrity and latency compared to conventional methods. Overall, this integrated approach sets a new benchmark for efficient, adaptive and intelligent smart grid communication, enabling future advancements in sustainable power system management.</p>

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Energy optimization in smart grids: leveraging gazelle algorithm and deep belief networks in IoT-connected sensor networks

  • 期刊论文

  • 发表日期：

    2026年1月5日

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  • G. Saravanan,
  • A. Yazhini,
  • Bapayya Naidu Kommula,
  • P. Mani

摘要

An emerging innovation in modern electrical power systems is the smart grid, which integrates advanced communication and control technologies to enhance the reliability and efficiency of traditional energy networks. To achieve optimal performance, continuous monitoring and intelligent control are essential. This research focuses on precise power generation tracking in smart grid environments using Wireless Sensor Networks (WSNs). A connection is established between power generation units and specialized sensor nodes to enable seamless and real-time data exchange. To address challenges related to energy efficiency and scalability, an enhanced Cluster Head (CH) selection strategy based on the Gazelle Optimization Algorithm (GOA) is proposed. Furthermore, a Deep Belief Network (DBN) is employed to ensure intelligent and efficient data transmission by minimizing routing distance and improving network lifetime. The proposed framework demonstrates significant improvements in data throughput, packet integrity and latency compared to conventional methods. Overall, this integrated approach sets a new benchmark for efficient, adaptive and intelligent smart grid communication, enabling future advancements in sustainable power system management.