# Load Disaggregation Based on Aided Linear Integer Programming

arXiv: https://arxiv.org/abs/1603.07417 · HTML: https://mlsanity.com/paper/1603.07417/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2016.

## Built on by (most cited first, 4 of 4)

- [Mixed-Integer Nonlinear Programming for State-based Non-Intrusive Load Monitoring](https://mlsanity.com/paper/2106.09158/) (2021)
- [A Robust Approach for the Decomposition of High-Energy-Consuming Industrial Loads with Deep Learning](https://mlsanity.com/paper/2203.07075/) (2022)
- [A Scoping Review of Energy Load Disaggregation](https://mlsanity.com/paper/2402.01654/) (2024)
- [More Behind Your Electricity Bill: a Dual-DNN Approach to Non-Intrusive Load Monitoring](https://mlsanity.com/paper/2106.00297/) (2021)

## Abstract (arXiv)

Load disaggregation based on aided linear integer programming (ALIP) is proposed. We start with a conventional linear integer programming (IP) based disaggregation and enhance it in several ways. The enhancements include additional constraints, correction based on a state diagram, median filtering, and linear programming-based refinement. With the aid of these enhancements, the performance of IP-based disaggregation is significantly improved. The proposed ALIP system relies only on the instantaneous load samples instead of waveform signatures, and hence does not crucially depend on high sampling frequency. Experimental results show that the proposed ALIP system performs better than the conventional IP-based load disaggregation system.