# On the Feasibility of Generic Deep Disaggregation for Single-Load Extraction

arXiv: https://arxiv.org/abs/1802.02139 · HTML: https://mlsanity.com/paper/1802.02139/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2018.

## Built on by (most cited first, 1 of 1)

- [Fed-NILM: A Federated Learning-based Non-Intrusive Load Monitoring Method for Privacy-Protection](https://mlsanity.com/paper/2105.11085/) (2021)

## Abstract (arXiv)

Recently, and with the growing development of big energy datasets, data-driven learning techniques began to represent a potential solution to the energy disaggregation problem outperforming engineered and hand-crafted models. However, most proposed deep disaggregation models are load-dependent in the sense that either expert knowledge or a hyper-parameter optimization stage is required prior to training and deployment (normally for each load category) even upon acquisition and cleansing of aggregate and sub-metered data. In this paper, we present a feasibility study on the development of a generic disaggregation model based on data-driven learning. Specifically, we present a generic deep disaggregation model capable of achieving state-of-art performance in load monitoring for a variety of load categories. The developed model is evaluated on the publicly available UK-DALE dataset with a moderately low sampling frequency and various domestic loads.