Temporal Mining Approaches for Smart Buildings Research
Abstract
With the advent of modern sensor technologies, significant opportunities have opened up to help
conserve energy in residential and commercial buildings. Moreover, the rapid urbanization we are
witnessing requires optimized energy distribution. This dissertation focuses on two sub-problems
in improving energy conservation; energy disaggregation and occupancy prediction. Energy disaggregation
attempts to separate the energy usage of each circuit or each electric device in a building
using only aggregate electricity usage information from the meter for the whole house. The second
problem of occupancy prediction can be accomplished using non-invasive indoor activity tracking
to predict the locations of people inside a building. We cast both problems as temporal mining
problems. We exploit motif mining with constraints to distinguish devices with multiple states,
which helps tackle the energy disaggregation problem. Our results reveal that motif mining is
adept at distinguishing devices with multiple power levels and at disentangling the combinatorial
operation of devices. For the second problem we propose time-gap constrained episode mining
to detect activity patterns followed by the use of a mixture of episode generating HMM (EGH)
models to predict home occupancy. Finally, we demonstrate that the mixture EGH model can also
help predict the location of a person to address non-invasive indoor activities tracking.
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