An intelligent power monitoring and analysis system for distributed smart plugs sensor networks

Shih Hsiung Lee, Chu Sing Yang

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

With the growth in power demand, energy management is an important issue in the 21st century. This article proposes a smart power management framework system, which comprises three parts. Part 1: a smart plug. Controlling the switching power supply, different sensors can be mounted for different application environments. The power supply can be switched on/off automatically according to environmental changes. Added to this, it can measure voltage and current for analysis. Part 2: a smart gateway. This can act as the mediation module for communication and implement the concept of fog computing. The local inference model is built and deployed by deep learning, and the model is learned, updated, and improved continuously to increase the intelligent control efficiency. Part 3: a management platform and mobile app. This allows for data visualization and a remote control for a user interface medium for scheduling. The smart plug and smart gateway are integrated into the overall distributed sensor network, analyzing and improving the power consumption effectively. Finally, the feasibility and practicability of the overall power management framework system are described experimentally.

Original languageEnglish
JournalInternational Journal of Distributed Sensor Networks
Volume13
Issue number7
DOIs
Publication statusPublished - 2017 Jul 1

Fingerprint

Sensor networks
Gateways (computer networks)
Data visualization
Monitoring
Intelligent control
Fog
Remote control
Application programs
User interfaces
Electric power utilization
Scheduling
Communication
Sensors
Electric potential
Power management
Demand side management
Deep learning

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Networks and Communications

Cite this

@article{c3133a0f39694a4cbc197947a7f28773,
title = "An intelligent power monitoring and analysis system for distributed smart plugs sensor networks",
abstract = "With the growth in power demand, energy management is an important issue in the 21st century. This article proposes a smart power management framework system, which comprises three parts. Part 1: a smart plug. Controlling the switching power supply, different sensors can be mounted for different application environments. The power supply can be switched on/off automatically according to environmental changes. Added to this, it can measure voltage and current for analysis. Part 2: a smart gateway. This can act as the mediation module for communication and implement the concept of fog computing. The local inference model is built and deployed by deep learning, and the model is learned, updated, and improved continuously to increase the intelligent control efficiency. Part 3: a management platform and mobile app. This allows for data visualization and a remote control for a user interface medium for scheduling. The smart plug and smart gateway are integrated into the overall distributed sensor network, analyzing and improving the power consumption effectively. Finally, the feasibility and practicability of the overall power management framework system are described experimentally.",
author = "Lee, {Shih Hsiung} and Yang, {Chu Sing}",
year = "2017",
month = "7",
day = "1",
doi = "10.1177/1550147717718462",
language = "English",
volume = "13",
journal = "International Journal of Distributed Sensor Networks",
issn = "1550-1329",
publisher = "Hindawi Publishing Corporation",
number = "7",

}

TY - JOUR

T1 - An intelligent power monitoring and analysis system for distributed smart plugs sensor networks

AU - Lee, Shih Hsiung

AU - Yang, Chu Sing

PY - 2017/7/1

Y1 - 2017/7/1

N2 - With the growth in power demand, energy management is an important issue in the 21st century. This article proposes a smart power management framework system, which comprises three parts. Part 1: a smart plug. Controlling the switching power supply, different sensors can be mounted for different application environments. The power supply can be switched on/off automatically according to environmental changes. Added to this, it can measure voltage and current for analysis. Part 2: a smart gateway. This can act as the mediation module for communication and implement the concept of fog computing. The local inference model is built and deployed by deep learning, and the model is learned, updated, and improved continuously to increase the intelligent control efficiency. Part 3: a management platform and mobile app. This allows for data visualization and a remote control for a user interface medium for scheduling. The smart plug and smart gateway are integrated into the overall distributed sensor network, analyzing and improving the power consumption effectively. Finally, the feasibility and practicability of the overall power management framework system are described experimentally.

AB - With the growth in power demand, energy management is an important issue in the 21st century. This article proposes a smart power management framework system, which comprises three parts. Part 1: a smart plug. Controlling the switching power supply, different sensors can be mounted for different application environments. The power supply can be switched on/off automatically according to environmental changes. Added to this, it can measure voltage and current for analysis. Part 2: a smart gateway. This can act as the mediation module for communication and implement the concept of fog computing. The local inference model is built and deployed by deep learning, and the model is learned, updated, and improved continuously to increase the intelligent control efficiency. Part 3: a management platform and mobile app. This allows for data visualization and a remote control for a user interface medium for scheduling. The smart plug and smart gateway are integrated into the overall distributed sensor network, analyzing and improving the power consumption effectively. Finally, the feasibility and practicability of the overall power management framework system are described experimentally.

UR - http://www.scopus.com/inward/record.url?scp=85026754614&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85026754614&partnerID=8YFLogxK

U2 - 10.1177/1550147717718462

DO - 10.1177/1550147717718462

M3 - Article

AN - SCOPUS:85026754614

VL - 13

JO - International Journal of Distributed Sensor Networks

JF - International Journal of Distributed Sensor Networks

SN - 1550-1329

IS - 7

ER -