Deep Sparse Coding for Non-Intrusive Load Monitoring

TL;DR

This paper introduces a deep learning approach with multi-layer dictionaries for energy disaggregation, outperforming existing shallow dictionary methods on benchmark datasets and real-world implementation.

Contribution

It proposes a novel deep sparse coding method with multiple dictionary layers for non-intrusive load monitoring, advancing beyond traditional shallow models.

Findings

Outperforms state-of-the-art techniques on benchmark datasets.

Effective in real-world energy disaggregation scenarios.

Demonstrates the advantages of deep dictionary learning over shallow models.

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. The traditional way to address this is via stochastic finite state machines (e.g. Factorial Hidden Markov Model). 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. Prior studies in this area are shallow learning techniques, i.e. they learn a single layer of dictionary for every device. In this work, we propose a deep learning approach, instead of learning one level of dictionary, we learn multiple layers of dictionaries for each device. These multi-level dictionaries are used as a basis for source separation during disaggregation. Results on two benchmark datasets and one actual implementation show that our method outperforms state-of-the-art techniques.