Artificial neural network based controller for home energy management considering demand response events

Maytham S. Ahmed, Azah Mohamed, Hussain Shareef, Raad Z. Homod, Jamal Abd Ali

Research output: Chapter in Book/Report/Conference proceedingConference contribution

47 Citations (Scopus)

Abstract

Electricity demand response and residential load modeling play important roles in the development of home energy management system. Accurate load models are required to produce a load profile at residential level. In this paper, modeling of four load types that include air conditioner, electric water heater, washing machine, and refrigerator are developed considering customer lifestyle and priority by using Matlab/ Simulink. In addition, the home energy management controller is proposed using artificial neural network (ANN) to predict the optimal ON/OFF status of the home appliances. The feedforward neural network type and Levenberg-Marquardt (LM) training algorithm are chosen for training the ANN in the Matlab toolbox. Results showed that the proposed ANN based controller can decrease the energy consumption for home appliances at specific time and can maintain the total household power consumption below its demand limit without affecting customer lifestyles.

Original languageEnglish
Title of host publication2016 International Conference on Advances in Electrical, Electronic and Systems Engineering, ICAEES 2016
EditorsRosdiadee Nordin, Mohd Fais Mansor, Mahamod Ismail
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages506-509
Number of pages4
ISBN (Electronic)9781509028894
DOIs
Publication statusPublished - Mar 27 2017
Event2016 International Conference on Advances in Electrical, Electronic and Systems Engineering, ICAEES 2016 - Putrajaya, Malaysia
Duration: Nov 14 2016Nov 16 2016

Publication series

Name2016 International Conference on Advances in Electrical, Electronic and Systems Engineering, ICAEES 2016

Conference

Conference2016 International Conference on Advances in Electrical, Electronic and Systems Engineering, ICAEES 2016
Country/TerritoryMalaysia
CityPutrajaya
Period11/14/1611/16/16

Keywords

  • Artificial neural network (ANN)
  • Energy efficiency
  • Home appliance
  • Home energy management system
  • Load scheduling
  • Residential demand response

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Biomedical Engineering
  • Control and Systems Engineering
  • Hardware and Architecture
  • Computer Networks and Communications
  • Instrumentation

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