  Understanding and predicting the electric consumption patterns in the short-,
mid- and long-term, at the distribution and transmission level, is a
fundamental asset for smart grids infrastructure planning, dynamic network
reconfiguration, dynamic energy pricing and savings, and thus energy
efficiency. This work introduces the Functional Principal Component Analysis
(FPCA) as a versatile method to both investigate and predict, at different
level of spatial aggregation, the consumption patterns. The method was applied
to a unique and sensitive dataset that includes electric consumption and
contractual information of Milan metropolitan area. The decomposition of the
load patterns into principal functions was found to be a powerful method to
identify the physical and behavioral causes underlying the daily consumptions,
given knowledge of exogenous variables such as calendar and meteorological
data. The effectiveness of long-term predictions based on principal functions
was proved on Milan's metropolitan area data and assessed on a
publicly-available dataset.
