An L0-Norm Constrained Non-Negative Matrix Factorization Algorithm for the Simultaneous Disaggregation of Fixed and Shiftable Loads

TL;DR

This paper introduces a novel L0-norm constrained non-negative matrix factorization algorithm for energy disaggregation, effectively separating fixed and shiftable loads in household energy data with improved computational efficiency.

Contribution

It proposes a new NMF-based method that directly applies L0 constraints to shiftable loads, eliminating the need for complex spectral or annealing techniques.

Findings

Effective disaggregation of household energy data into fixed and shiftable loads.

Reduces computational complexity compared to previous methods.

Validated with real consumer energy data.

Abstract

Energy disaggregation refers to the decomposition of energy use time series data into its constituent loads. This paper decomposes daily use data of a household unit into fixed loads and one or more classes of shiftable loads. The latter is characterized by ON OFF duty cycles. A novel algorithm based on nonnegative matrix factorization NMF for energy disaggregation is proposed, where fixed loads are represented in terms of real-valued basis vectors, whereas shiftable loads are divided into binary signals. This binary decomposition approach directly applies L0 norm constraints on individual shiftable loads. The new approach obviates the need for more computationally intensive methods e.g. spectral decomposition or mean field annealing that have been used in earlier research for these constraints. A probabilistic framework for the proposed approach has been addressed. The proposed approach s effectiveness has been demonstrated with real consumer energy data.