  Non-intrusive load monitoring (NILM) or energy disaggregation aims to break
down total household energy consumption into constituent appliances. Prior work
has shown that providing an energy breakdown can help people save up to 15\% of
energy. In recent years, deep neural networks (deep NNs) have made remarkable
progress in the domain of NILM. In this paper, we demonstrate the performance
of Gaussian Processes (GPs) for NILM. We choose GPs due to three main reasons:
i) GPs inherently model uncertainty; ii) equivalence between infinite NNs and
GPs; iii) by appropriately designing the kernel we can incorporate domain
expertise. We explore and present the challenges of applying our GP approaches
to NILM.
