Non-Intrusive Energy Disaggregation Using Non-negative Matrix Factorization with Sum-to-k Constraint

Abstract

Energy disaggregation or Non-Intrusive Load Monitoring (NILM) addresses the issue of extracting device-level energy consumption information by monitoring the aggregated signal at one single measurement point without installing meters on each individual device. Energy disaggregation can be formulated as a source separation problem where the aggregated signal is expressed as linear combination of basis vectors in a matrix factorization framework. In this paper, an approach based on Sum-to-k constrained Non-negative Matrix Factorization (S2K-NMF) is proposed. By imposing the sum-to-k constraint and the non-negative constraint, S2K-NMF is able to effectively extract perceptually meaningful sources from complex mixtures. The strength of the proposed algorithm is demonstrated through two sets of experiments: Energy disaggregation in a residential smart home, and HVAC components energy monitoring in an industrial building testbed maintained at the Oak Ridge National Laboratory (ORNL). Extensive experimental results demonstrate the superior performance of S2K-NMF as compared to state-of-the-art decomposition-based disaggregation algorithms. The source code and our collected data (HVORUT) for studying NILM for HVAC units can be found at https://bitbucket.org/aicip/nonintrusive-load-monitoring.

New likes added to previous version. This article has been published in IEEE Transactions on Power Systems-2017

References in corpus (2)

• Does disaggregated electricity feedback reduce domestic electricity consumption? A systematic review of the literature
• NESTT: A Nonconvex Primal-Dual Splitting Method for Distributed and Stochastic Optimization

Cited by in corpus (11)

• Improving Non-Intrusive Load Disaggregation through an Attention-Based Deep Neural Network
• Smart non-intrusive appliance identification using a novel local power histogramming descriptor with an improved k-nearest neighbors classifier
• Unsupervised algorithm for disaggregating low-sampling-rate electricity consumption of households
• Mixed-Integer Nonlinear Programming for State-based Non-Intrusive Load Monitoring
• Energy Efficient Deep Multi-Label ON/OFF Classification of Low Frequency Metered Home Appliances
• Structured Dictionary Learning for Energy Disaggregation
• Activity Detection And Modeling Using Smart Meter Data: Concept And Case Studies
• Robust Matrix Factorization with Grouping Effect
• A Data-Driven Machine Learning Approach for Consumer Modeling with Load Disaggregation
• PHASED: Phase-Aware Submodularity-Based Energy Disaggregation
• An L0-Norm Constrained Non-Negative Matrix Factorization Algorithm for the Simultaneous Disaggregation of Fixed and Shiftable Loads