# Scalable Hybrid Classification-Regression Solution for High-Frequency Nonintrusive Load Monitoring

arXiv: https://arxiv.org/abs/2208.10638 · HTML: https://mlsanity.com/paper/2208.10638/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 32 references that are in the map.
- First posted on arXiv 22 Aug 2022.

## Builds on (most cited first, 4 of 4)

- [Neural NILM: Deep Neural Networks Applied to Energy Disaggregation](https://mlsanity.com/paper/1507.06594/) (2015)
- [Active Collaborative Sensing for Energy Breakdown](https://mlsanity.com/paper/1909.00525/) (2019)
- [An Empirical Investigation of V-I Trajectory based Load Signatures for Non-Intrusive Load Monitoring](https://mlsanity.com/paper/1305.0596/) (2013)
- [Distributed Power Apportioning with Early Dispatch for Ancillary Services in Renewable Grids](https://mlsanity.com/paper/2007.11715/) (2020)

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

- [Real Time NILM Based Power Monitoring of Identical Induction Motors Representing Cutting Machines in Textile Industry](https://mlsanity.com/paper/2601.01616/) (2026)

## Abstract (arXiv)

Residential buildings with the ability to monitor and control their net-load (sum of load and generation) can provide valuable flexibility to power grid operators. We present a novel multiclass nonintrusive load monitoring (NILM) approach that enables effective net-load monitoring capabilities at high-frequency with minimal additional equipment and cost. The proposed machine learning based solution provides accurate multiclass state predictions while operating at a faster timescale (able to provide a prediction for each 60-Hz ac cycle used in US power grid) without relying on event-detection techniques. We also introduce an innovative hybrid classification-regression method that allows for the prediction of not only load on/off states via classification but also individual load operating power levels via regression. A test bed with eight residential appliances is used for validating the NILM approach. Results show that the overall method has high accuracy and, good scaling and generalization properties. Furthermore, the method is shown to have sufficient response time (within 160ms, corresponding to 10 ac cycles) to support building grid-interactive control at fast timescales relevant to the provision of grid frequency support services.