  Electricity consumed by residential consumers counts for a significant part
of global electricity consumption and utility companies can collect
high-resolution load data thanks to the widely deployed advanced metering
infrastructure. There has been a growing research interest toward appliance
load disaggregation via nonintrusive load monitoring. As the electricity
consumption of appliances is directly associated with the activities of
consumers, this paper proposes a new and more effective approach, i.e.,
activity disaggregation. We present the concept of activity disaggregation and
discuss its advantage over traditional appliance load disaggregation. We
develop a framework by leverage machine learning for activity detection based
on residential load data and features. We show through numerical case studies
to demonstrate the effectiveness of the activity detection method and analyze
consumer behaviors by time-dependent activity modeling. Last but not least, we
discuss some potential use cases that can benefit from activity disaggregation
and some future research directions.
