  Non-Intrusive Load Monitoring (NILM) is an energy efficiency technique to
track electricity consumption of an individual appliance in a household by one
aggregated single, such as building level meter readings. The goal of NILM is
to disaggregate the appliance from the aggregated singles by computational
method. In this work, deep learning approaches are implemented to operate the
desegregations. Deep neural networks, convolutional neural networks, and
recurrent neural networks are employed for this operation. Additionally, sparse
evolutionary training is applied to accelerate training efficiency of each deep
learning model. UK-Dale dataset is used for this work.
