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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
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
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