# Lightweight and Scalable Transfer Learning Framework for Load Disaggregation

arXiv: https://arxiv.org/abs/2603.04998 · HTML: https://mlsanity.com/paper/2603.04998/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 23 references that are in the map.
- First posted on arXiv 5 Mar 2026.

## Builds on (most cited first, 3 of 3)

- [Transfer Learning for Non-Intrusive Load Monitoring](https://mlsanity.com/paper/1902.08835/) (2019)
- [MATNilm: Multi-appliance-task Non-intrusive Load Monitoring with Limited Labeled Data](https://mlsanity.com/paper/2307.14778/) (2023)
- [Non-intrusive Load Monitoring based on Self-supervised Learning](https://mlsanity.com/paper/2210.04176/) (2022)

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

- [Ask for Any Appliance: A Prompt-Programmable Foundation Model for Non-Intrusive Load Monitoring](https://mlsanity.com/paper/2609.23146/) (2026)

## Abstract (arXiv)

Non-Intrusive Load Monitoring (NILM) aims to estimate appliance-level consumption from aggregate electrical signals recorded at a single measurement point. In recent years, the field has increasingly adopted deep learning approaches; however, cross-domain generalization remains a persistent challenge due to variations in appliance characteristics, usage patterns, and background loads across homes. Transfer learning provides a practical paradigm to adapt models with limited target data. However, existing methods often assume a fixed appliance set, lack flexibility for evolving real-world deployments, remain unsuitable for edge devices, or scale poorly for real-time operation. This paper proposes RefQuery, a scalable multi-appliance, multi-task NILM framework that conditions disaggregation on compact appliance fingerprints, allowing one shared model to serve many appliances without a fixed output set. RefQuery keeps a pretrained disaggregation network fully frozen and adapts to a target home by learning only a per-appliance embedding during a lightweight backpropagation stage. Experiments on three public datasets demonstrate that RefQuery delivers a strong accuracy-efficiency trade-off against single-appliance and multi-appliance baselines, including modern Transformer-based methods. These results support RefQuery as a practical path toward scalable, real-time NILM on resource-constrained edge devices.