Mixed-Integer Nonlinear Programming for State-based Non-Intrusive Load Monitoring

TL;DR

This paper introduces a novel two-stage mixed-integer nonlinear programming approach for energy disaggregation in NILM, improving accuracy and efficiency over existing methods by incorporating prior knowledge and appliance constraints.

Contribution

The paper presents a new optimization-based framework for NILM that effectively disambiguates similar loads and handles unmetered devices, outperforming previous algorithms.

Findings

Accurately disaggregates energy consumption in real-world datasets.

Handles appliances with similar consumption patterns effectively.

Overcomes limitations of existing optimization-based NILM methods.

Abstract

Energy disaggregation, known in the literature as Non-Intrusive Load Monitoring (NILM), is the task of inferring the energy consumption of each appliance given the aggregate signal recorded by a single smart meter. In this paper, we propose a novel two-stage optimization-based approach for energy disaggregation. In the first phase, a small training set consisting of disaggregated power profiles is used to estimate the parameters and the power states by solving a mixed integer programming problem. Once the model parameters are estimated, the energy disaggregation problem is formulated as a constrained binary quadratic optimization problem. We incorporate penalty terms that exploit prior knowledge on how the disaggregated traces are generated, and appliance-specific constraints characterizing the signature of different types of appliances operating simultaneously. Our approach is compared with existing optimization-based algorithms both on a synthetic dataset and on three real-world datasets. The proposed formulation is computationally efficient, able to disambiguate loads with similar consumption patterns, and successfully reconstruct the signatures of known appliances despite the presence of unmetered devices, thus overcoming the main drawbacks of the optimization-based methods available in the literature.