Load Disaggregation Based on Aided Linear Integer Programming

TL;DR

This paper introduces an improved load disaggregation method called ALIP that enhances linear integer programming with additional constraints and filtering, achieving better performance without relying on waveform signatures.

Contribution

The paper presents ALIP, a novel load disaggregation approach that improves conventional IP-based methods through multiple enhancements and operates effectively with only instantaneous load samples.

Findings

ALIP outperforms conventional IP-based disaggregation.

ALIP does not depend on waveform signatures or high sampling frequency.

Experimental results confirm improved accuracy and robustness.

Abstract

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.