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<p>Energy Management of PV-powered lighting systems for smart cities integrates RES, such as PV panels, WT, battery ESS, and the grid to optimize energy utilization, enhance sustainability, and reduce reliance on conventional energy sources. At the same time, the system has to address problems in balancing costs and efficiency, because the rise in RE like PV and wind, and the need for ESS and grid integration, can make it harder to match supply and demand. To address these issues, this paper proposes a hybrid strategy for EM in PV-powered lighting systems for smart cities. The hybrid method integrates the POA and GENN. The main aim is to reduce operational costs and improve the energy efficiency of the PV-powered lighting system. POA is utilized to optimize energy allocation among RES, the grid, and energy storage, enhancing resource utilization and maintaining supply-demand balance. While GENN is utilized to predict energy generation and consumption patterns, it enhances the accuracy of forecasting for improved system performance. By then, the proposed method is implemented on the MATLAB platform and evaluated with various existing approaches. The POA-GENN approach achieves an operational cost of 365.24 €ct and an efficiency of 99.2%, demonstrating its effectiveness in optimizing the EM of PV-powered lighting systems for smart cities.</p>

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Optimized energy management of PV-Powered lighting system for smart cities using perfumer optimization algorithm and graph ensemble neural network

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月17日

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  • Zakir Hussain,
  • Prabu Selvam,
  • M. Sivaramkrishnan,
  • M. Siva Ramkumar,
  • Sivaprakash Paramasivam,
  • Mohamed Ghouse,
  • T. M. Yunus Khan,
  • Md Amir Khan,
  • Amanuel Zewdie

摘要

Energy Management of PV-powered lighting systems for smart cities integrates RES, such as PV panels, WT, battery ESS, and the grid to optimize energy utilization, enhance sustainability, and reduce reliance on conventional energy sources. At the same time, the system has to address problems in balancing costs and efficiency, because the rise in RE like PV and wind, and the need for ESS and grid integration, can make it harder to match supply and demand. To address these issues, this paper proposes a hybrid strategy for EM in PV-powered lighting systems for smart cities. The hybrid method integrates the POA and GENN. The main aim is to reduce operational costs and improve the energy efficiency of the PV-powered lighting system. POA is utilized to optimize energy allocation among RES, the grid, and energy storage, enhancing resource utilization and maintaining supply-demand balance. While GENN is utilized to predict energy generation and consumption patterns, it enhances the accuracy of forecasting for improved system performance. By then, the proposed method is implemented on the MATLAB platform and evaluated with various existing approaches. The POA-GENN approach achieves an operational cost of 365.24 €ct and an efficiency of 99.2%, demonstrating its effectiveness in optimizing the EM of PV-powered lighting systems for smart cities.