Mining typical load profiles in buildings to
drive energy management strategies is a fundamental
task
to be addressed in a smart
city environment. In this work,
a general framework
on load profiles characterisation in buildings based on the
recent
scientific
literature
is proposed
. The
process
relies on the combination of different pattern recognition and classification algorithms in order
to provide a robust insight of the energy usage patterns at different level
s and at different scales (from single building to stock of
buildings).
Several im
plications related to energy profiling in buildings, including tariff design, demand side management and
advanced energy diagnos
is are discussed.
Moreover,
a robust methodology
to mine typical energy patterns to
support advanced
energy
diagnosis
in buildin
gs is introduced
by analysing the monitored energy consumption of
a cooling/heating mechanical room
