PicoGrid Smart Home Energy Management System

Collin J. Daly, David L. Moore, Rami J. Haddad, Aaron Specht, Shaina Neal

Research output: Contribution to book or proceedingConference articlepeer-review

1 Scopus citations

Abstract

Due to increased automation in the home, the needed capacity of electrical distribution grids is continuously growing larger to accommodate peak usage, leading to underutilized capacity during nonpeak usage hours. To assist homeowners in identifying large electrical loads and wasted energy usage in the home environment, this paper proposes a novel method of measuring power usage and automatically identifying and classifying the device through the use of an artificial neural network. The result would be a method of reviewing energy usage per device connected to the picogrid over a defined interval regardless of which monitored outlet the device is connected to for utility power. The neural network classifier further provides the ability to track appliance performance over time and compare changes in power draw. Prototype testing of the proposed system has yielded promising results in both the ability to measure consumed power and to classify devices when connected to the metered outlet.

Original languageEnglish
Title of host publicationSoutheastcon 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538661338
DOIs
StatePublished - Oct 1 2018
Event2018 IEEE Southeastcon, Southeastcon 2018 - St. Petersburg, United States
Duration: Apr 19 2018Apr 22 2018

Publication series

NameConference Proceedings - IEEE SOUTHEASTCON
Volume2018-April
ISSN (Print)1091-0050
ISSN (Electronic)1558-058X

Conference

Conference2018 IEEE Southeastcon, Southeastcon 2018
Country/TerritoryUnited States
CitySt. Petersburg
Period04/19/1804/22/18

Keywords

  • Picogrid
  • energy management
  • power monitoring
  • smart home

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