Energy Disaggregation using Piecewise Affine Regression and Binary Quadratic Programming

In this paper we consider the problem of energy disaggregation, commonly referred in the literature as “non-intrusive load monitoring”. The problem is to estimate the end-use power consumption profiles of individual household appliance using only aggregated power measurements. We propose a two-stage supervised approach. At the first stage, dynamical models of individual appliances are estimated using disaggregated training data gathered over a short intrusive period. The consumption profiles of individual appliances are described by PieceWise Affine AutoRegressive (PWA-AR) models with multiple operating modes, which are estimated via a moving horizon PWA regression algorithm. Once the model of each appliance is identified, a binary quadratic programming problem is solved at the second stage to determine the set of active appliances which contribute to the instantaneous aggregated power, along with their operating modes. A benchmark dataset is used to assess the performance of the presented disaggregation approach.