Abstract—Demand Response (DR) and Time-of-Use (TOU)

pricing refer to programs which offer incentives to customers

who curtail their energy use during times of peak demand. In this

paper, we propose an integrated solution to predict and re-engineer

the electricity demand (e.g., peak load reduction and shift) in

a locality at a given day/time. The system presented in this paper

expands DR to residential loads by dynamically scheduling and

controlling appliances in each dwelling unit. A decision-support

system is developed to forecast electricity demand in the home and

enable the user to save energy by recommending optimal run time

schedules for appliances, given user constraints and TOU pricing

the utility company. The schedule is communicated to the

smart appliances over a self-organizing home energy network

and d by the appliance control interfaces developed in this-Abstract—Demand Response (DR) and Time-of-Use (TOU)

pricing refer to programs which offer incentives to customers

who curtail their energy use during times of peak demand. In this

paper, we propose an integrated solution to predict and re-engineer

the electricity demand (e.g., peak load reduction and shift) in

a locality at a given day/time. The system presented in this paper

expands DR to residential loads by dynamically scheduling and

controlling appliances in each dwelling unit. A decision-support

system is developed to forecast electricity demand in the home and

enable the user to save energy by recommending optimal run time

schedules for appliances, given user constraints and TOU pricing

the utility company. The schedule is communicated to the

smart appliances over a self-organizing home energy network

and　d by the appliance control interfaces developed in this