  Non-Intrusive Load Monitoring (NILM) aims to predict the status or
consumption of domestic appliances in a household only by knowing the
aggregated power load. NILM can be formulated as regression problem or most
often as a classification problem. Most datasets gathered by smart meters allow
to define naturally a regression problem, but the corresponding classification
problem is a derived one, since it requires a conversion from the power signal
to the status of each device by a thresholding method. We treat three different
thresholding methods to perform this task, discussing their differences on
various devices from the UK-DALE dataset. We analyze the performance of deep
learning state-of-the-art architectures on both the regression and
classification problems, introducing criteria to select the most convenient
thresholding method.
