  Increasing population indicates that energy demands need to be managed in the
residential sector. Prior studies have reflected that the customers tend to
reduce a significant amount of energy consumption if they are provided with
appliance-level feedback. This observation has increased the relevance of load
monitoring in today's tech-savvy world. Most of the previously proposed
solutions claim to perform load monitoring without intrusion, but they are not
completely non-intrusive. These methods require historical appliance-level data
for training the model for each of the devices. This data is gathered by
putting a sensor on each of the appliances present in the home which causes
intrusion in the building. Some recent studies have proposed that if we frame
Non-Intrusive Load Monitoring (NILM) as a multi-label classification problem,
the need for appliance-level data can be avoided. In this paper, we propose
Multi-label Restricted Boltzmann Machine(ML-RBM) for NILM and report an
experimental evaluation of proposed and state-of-the-art techniques.
