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

doi:10.1109/ICAEES.2016.7888097

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

Department of Electrical and Communication Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

65 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 language	English
Title of host publication	2016 International Conference on Advances in Electrical, Electronic and Systems Engineering, ICAEES 2016
Editors	Rosdiadee Nordin, Mohd Fais Mansor, Mahamod Ismail
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	506-509
Number of pages	4
ISBN (Electronic)	9781509028894
DOIs	https://doi.org/10.1109/ICAEES.2016.7888097
Publication status	Published - 2016
Event	2016 International Conference on Advances in Electrical, Electronic and Systems Engineering, ICAEES 2016 - Putrajaya, Malaysia Duration: Nov 14 2016 → Nov 16 2016

Publication series

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

Conference

Conference	2016 International Conference on Advances in Electrical, Electronic and Systems Engineering, ICAEES 2016
Country/Territory	Malaysia
City	Putrajaya
Period	11/14/16 → 11/16/16

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

Access to Document

10.1109/ICAEES.2016.7888097

Cite this

Ahmed, M. S., Mohamed, A., Shareef, H., Homod, R. Z., & Ali, J. A. (2016). Artificial neural network based controller for home energy management considering demand response events. In R. Nordin, M. F. Mansor, & M. Ismail (Eds.), 2016 International Conference on Advances in Electrical, Electronic and Systems Engineering, ICAEES 2016 (pp. 506-509). Article 7888097 (2016 International Conference on Advances in Electrical, Electronic and Systems Engineering, ICAEES 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICAEES.2016.7888097

Artificial neural network based controller for home energy management considering demand response events. / Ahmed, Maytham S.; Mohamed, Azah; Shareef, Hussain et al.
2016 International Conference on Advances in Electrical, Electronic and Systems Engineering, ICAEES 2016. ed. / Rosdiadee Nordin; Mohd Fais Mansor; Mahamod Ismail. Institute of Electrical and Electronics Engineers Inc., 2016. p. 506-509 7888097 (2016 International Conference on Advances in Electrical, Electronic and Systems Engineering, ICAEES 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Ahmed, MS, Mohamed, A, Shareef, H, Homod, RZ & Ali, JA 2016, Artificial neural network based controller for home energy management considering demand response events. in R Nordin, MF Mansor & M Ismail (eds), 2016 International Conference on Advances in Electrical, Electronic and Systems Engineering, ICAEES 2016., 7888097, 2016 International Conference on Advances in Electrical, Electronic and Systems Engineering, ICAEES 2016, Institute of Electrical and Electronics Engineers Inc., pp. 506-509, 2016 International Conference on Advances in Electrical, Electronic and Systems Engineering, ICAEES 2016, Putrajaya, Malaysia, 11/14/16. https://doi.org/10.1109/ICAEES.2016.7888097

Ahmed MS, Mohamed A, Shareef H, Homod RZ, Ali JA. Artificial neural network based controller for home energy management considering demand response events. In Nordin R, Mansor MF, Ismail M, editors, 2016 International Conference on Advances in Electrical, Electronic and Systems Engineering, ICAEES 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 506-509. 7888097. (2016 International Conference on Advances in Electrical, Electronic and Systems Engineering, ICAEES 2016). doi: 10.1109/ICAEES.2016.7888097

Ahmed, Maytham S. ; Mohamed, Azah ; Shareef, Hussain et al. / Artificial neural network based controller for home energy management considering demand response events. 2016 International Conference on Advances in Electrical, Electronic and Systems Engineering, ICAEES 2016. editor / Rosdiadee Nordin ; Mohd Fais Mansor ; Mahamod Ismail. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 506-509 (2016 International Conference on Advances in Electrical, Electronic and Systems Engineering, ICAEES 2016).

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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.",

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AB - 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.

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

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