Analysis Co-Sparse Coding for Energy Disaggregation

TL;DR

This paper introduces an analysis co-sparse coding approach for energy disaggregation that achieves comparable accuracy to existing methods but requires significantly less training data, reducing sensing costs.

Contribution

The work proposes a novel analysis-based dictionary learning method for energy disaggregation, lowering training data requirements compared to synthesis-based approaches.

Findings

Achieves similar disaggregation accuracy as state-of-the-art methods.

Requires fewer homes or days of data for training.

Reduces sensing costs significantly.

Abstract

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.