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DeepEdge: A Novel Appliance Identification Edge Platform for Data Gathering, Capturing and Labeling.

Zilin Wang1, Wei Wang1,2, Ziyou Zhang3

  • 1College of Computer Science, Sichuan University, Chengdu 610065, China.

Sensors (Basel, Switzerland)
|April 12, 2022
PubMed
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A new edge platform for appliance identification in smart grids offers a low-cost solution. This edge platform achieves 98.5% accuracy, improving non-intrusive load disaggregation for the Internet of Things.

Area of Science:

  • Electrical Engineering
  • Computer Science
  • Energy Systems

Background:

  • The Internet of Things (IoT) for smart grids necessitates effective appliance monitoring.
  • Appliance identification is crucial for monitoring but existing cloud-based platforms are resource-intensive.
  • There is a need for cost-effective, edge-based solutions for appliance identification.

Purpose of the Study:

  • To propose a novel edge identification platform for appliance monitoring in smart grids.
  • To develop a low-cost platform for data gathering, capturing, and labeling of appliance usage.
  • To evaluate the performance of the proposed edge platform in terms of accuracy and impact on load disaggregation.

Main Methods:

  • Development of a novel edge computing platform for appliance identification.
Keywords:
Internet of Thingsappliance identificationload monitoringtiny machine learning

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  • Implementation of data gathering, capturing, and labeling functionalities at the edge.
  • Experimental validation of the platform's performance and its effect on non-intrusive load disaggregation.

    • Main Results:

    • The proposed edge platform achieved an average appliance identification accuracy of 98.5%.
    • The platform demonstrated improved accuracy for non-intrusive load disaggregation algorithms.
    • The edge-based approach offers a low-cost alternative to cloud-based solutions.

    Conclusions:

    • The developed edge platform is effective for appliance identification in smart grid applications.
    • This edge solution reduces computational resource requirements compared to cloud-based systems.
    • The platform enhances the accuracy of load disaggregation, contributing to smarter energy management.