# Challenges in Gaussian Processes for Non Intrusive Load Monitoring

arXiv: https://arxiv.org/abs/2211.13018 · HTML: https://mlsanity.com/paper/2211.13018/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 14 references that are in the map.
- First posted on arXiv 18 Nov 2022.

## Builds on (most cited first, 2 of 2)

- [Neural NILM: Deep Neural Networks Applied to Energy Disaggregation](https://mlsanity.com/paper/1507.06594/) (2015)
- [Sequence-to-point learning with neural networks for nonintrusive load monitoring](https://mlsanity.com/paper/1612.09106/) (2016)

## Abstract (arXiv)

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.