Home EMS controlled by Neural Networks

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The widespread use of electricity in the most varied sectors, among them the industrial and domestic sectors adding up to the largest amount of energy consumed in recent years. Currently, this dependence happens because there are several equipments that need electricity uninterruptedly for correct operation, such as security systems, refrigerating equipment and others. The increase in equipments present in homes is also a relevant factor, leading to greater consumption of electricity and consequently an increase in the electric bill. In order to obtain improvements during the use of electricity, Neural Networks were used, which allowed constant learning and the use of previous prices. In this way, Artificial Intelligence software was used, which is increasingly sought after, and which has allowed for numerous operations such as event forecasting. Due to the points presented above, the idea arose of creating a tool that would make it possible to predict electricity prices and, conclusively, to manage loads that would be displaced and allocated in temporal spaces where the price of electricity was lower. The implementation of this tool has as its main objective to provide a reduction in the electricity bill whenever good load management occurs based on the forecast coming from the implemented neuronal network
Original languageEnglish
Title of host publicationIECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)978-1-6654-3554-3
ISBN (Print)978-1-6654-0256-9
DOIs
Publication statusPublished - 13 Oct 2021
Event47th Annual Conference of the IEEE Industrial Electronics Society - Toronto, Canada
Duration: 13 Oct 202116 Oct 2021

Publication series

NameIECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISSN (Print)1553-572X
ISSN (Electronic)2577-1647

Conference

Conference47th Annual Conference of the IEEE Industrial Electronics Society
Country/TerritoryCanada
CityToronto
Period13/10/2116/10/21

Keywords

  • Artificial Intelligence
  • C
  • Domestic electricity consumption
  • Neural Network
  • Price forecast

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