Value of aggregation in smart grids

The projected smart grid intends to deliver a variety of services to energy consumers. Many of these services rely on aggregates of energy consumers. Forecasting energy consumption of groups of users is a crucial aspect of this process. This paper addresses electricity load forecasting on varying scales of aggregation. Using city wide consumption data at 1 hour interval standard forecasting methods are applied to the data. It is shown that under some ideal conditions, forecasting errors decrease on as Θ(1/√N). We show empirically the true forecasting errors decrease as Θ(1/N^α) with α <; 0.5.