  The so-called Internet of Things (IoT) and advanced communication
technologies have already demonstrated a great potential to manage residential
energy resources via demand-side management. This work presents a home energy
management system in that focused on the energy reallocation problem where
consumers shall shift their energy consumption patterns away from peak periods
and/or high electricity prices. Our solution differentiates residential loads
into two categories: (i) fixed power appliances and (ii) flexible ones.
Therefrom, we formulate our problem as a constraint optimization problem, which
is non-linear and cannot be mathematically solved in closed-form. We then
employ and compare two well-known heuristics, the genetic algorithm (GA) and
the harmony search algorithm (HSA), to minimize electricity expense and peak to
average ratio. These two approaches are compared to the case where no
reallocation happens. Our numerical results show that both methods; GAand HSA
can effectively reduce the electricity cost by 0.9%, 3.98%, and PAR by 15%,
5.8%, respectively
