  Energy disaggregation is the task of segregating the aggregate energy of the
entire building (as logged by the smartmeter) into the energy consumed by
individual appliances. This is a single channel (the only channel being the
smart-meter) blind source (different electrical appliances) separation problem.
In recent times dictionary learning based approaches have shown promise in
addressing the disaggregation problem. The usual technique is to learn a
dictionary for every device and use the learnt dictionaries as basis for blind
source separation during disaggregation. Dictionary learning is a synthesis
formulation; in this work, we propose an analysis approach. The advantage of
our proposed approach is that, the requirement of training volume drastically
reduces compared to state-of-the-art techniques. This means that, we require
fewer instrumented homes, or fewer days of instrumentation per home; in either
case this drastically reduces the sensing cost. Results on two benchmark
datasets show that our method produces the same level of disaggregation
accuracy as state-of-the-art methods but with only a fraction of the training
data.
