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

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.