  There has been an increasing trend in the electric power system from a
centralized generation-driven grid to a more reliable, environmental friendly,
and customer-driven grid. One of the most important issues which the designers
of smart grids need to deal with is to forecast the fluctuations of power
demand and generation in order to make the power system facilities more
flexible to the variable nature of renewable power resources and demand-side.
This paper proposes a novel two-tier scheme for forecasting the power demand
and generation in a general residential electrical gird which uses the
distributed renewable resources as the primary energy resource. The proposed
forecasting scheme has two tiers: long-term demand/generation forecaster which
is based on Maximum-Likelihood Estimator (MLE) and real-time demand/generation
forecaster which is based on Auto-Regressive Integrated Moving-Average (ARIMA)
model. The paper also shows that how bulk generation improves the adequacy of
proposed residential system by canceling-out the forecasters estimation errors
which are in the form of Gaussian White noises.
