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Optimization Techniques and Evaluation for Building an Integrated Lightweight Platform for AI and Data Collection Systems on Low-Power Edge Devices

Author

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  • Woojin Cho

    (Energy Environment IT Convergence Group, Plant Engineering Center, Institute for Advanced Engineering, Yongin 17180, Republic of Korea)

  • Hyungah Lee

    (Energy Environment IT Convergence Group, Plant Engineering Center, Institute for Advanced Engineering, Yongin 17180, Republic of Korea)

  • Jae-hoi Gu

    (Energy Environment IT Convergence Group, Plant Engineering Center, Institute for Advanced Engineering, Yongin 17180, Republic of Korea)

Abstract

Amidst an energy crisis stemming from increased energy costs and the looming threat of war, there has been a burgeoning interest in energy conservation and management worldwide. Industrial complexes constitute a significant portion of total energy consumption. Hence, reducing energy consumption in these complexes is imperative for energy preservation. Typically, factories within similar industries aggregate in industrial complexes and share similar energy utilities. However, they often fail to capitalize on this shared infrastructure efficiently. To address this issue, a network system employing a virtual utility plant has been proposed. This system enables proactive measures to counteract energy surplus or deficit through AI-based predictions, thereby maximizing energy efficiency. Nevertheless, deploying conventional server systems within factories poses considerable challenges. Therefore, leveraging edge devices, characterized by low power consumption, high efficiency, and minimal space requirements, proves highly advantageous. Consequently, this study focuses on constructing and employing data collection and AI systems to utilize edge devices as standalone systems in each factory. To optimize the AI system for low-performance edge devices, we employed the integration-learning AI modeling technique. Evaluation results demonstrate that the proposed system exhibits high stability and reliability.

Suggested Citation

  • Woojin Cho & Hyungah Lee & Jae-hoi Gu, 2024. "Optimization Techniques and Evaluation for Building an Integrated Lightweight Platform for AI and Data Collection Systems on Low-Power Edge Devices," Energies, MDPI, vol. 17(7), pages 1-14, April.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:7:p:1757-:d:1371047
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    References listed on IDEAS

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    1. Hyungah Lee & Dongju Kim & Jae-Hoi Gu, 2023. "Prediction of Food Factory Energy Consumption Using MLP and SVR Algorithms," Energies, MDPI, vol. 16(3), pages 1-21, February.
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