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

Woojin Cho, Hyungah Lee and Jae-hoi Gu ()
Additional contact information
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

Energies, 2024, vol. 17, issue 7, 1-14

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.

Keywords: edge device; low-power computing; virtual plant utility; energy management; AI; deep learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/7/1757/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/7/1757/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:17:y:2024:i:7:p:1757-:d:1371047

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().