  In this paper, the modeling of building end-use energy profile is
comprehensively investigated. Top-down and Bottom-up approaches are discussed
with a focus on the latter for better integration with occupant information.
Compared to the Time-Of-Use (TOU) data used in previous Bottom-up models, this
work utilizes high frequency sampled appliance power consumption data from
wireless sensor network, and hence builds an appliance-data-driven probability
based end-use energy profile model. ON/OFF probabilities of appliances are used
in this model, to build a non-homogeneous Markov Chain, compared to the
duration statistics based model that is widely used in other works. The
simulation results show the capability of the model to capture the diversity
and variability of different categories of end-use appliance energy profile,
which can further help on the design of a modern robust building power system.
