Non-Intrusive Energy Disaggregation Using Non-Negative Matrix Factorization With Sum-to-k Constraint

Rahimpour, Alireza; Qi, Hairong; Fugate, David; Kuruganti, Teja

doi:10.1109/tpwrs.2017.2660246

Cited by 106 publications

(68 citation statements)

References 55 publications

(42 reference statements)

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“…The goal is then to recover the source signals from the mixture signal. Most of the proposed methods use low frequency power measurements [9,10,11] whereas Lange and Berges are using source separation on high frequency current measures [12]. NILM for commercial buildings started with Norford and Leeb's work [13].…”

Section: Related Workmentioning

confidence: 99%

A generative model for non-Intrusive load monitoring in commercial buildings

Henriet

Şimşekli

Fuentes³

et al. 2018

Energy and Buildings

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In the recent years, there has been an increasing academic and industrial interest for analyzing the electrical consumption of commercial buildings.Whilst having similarities with the Non Intrusive Load Monitoring (NILM) tasks for residential buildings, the nature of the signals that are collected from large commercial buildings introduces additional difficulties to the NILM research causing existing NILM approaches to fail. On the other hand, the amount of publicly available datasets collected from commercial buildings is very limited, which makes the NILM research even more challenging for this type of large buildings. In this study, we aim at addressing these issues.We first present an extensive statistical analysis of both commercial and residential measurements from public and private datasets and show important differences. Secondly, we develop an algorithm for generating synthetic current waveforms. We then demonstrate using real measurement and quantitative metrics that both our device model and our simulations are realistic and can be used to evaluate NILM algorithms. Finally, to encourage research on commercial buildings we release a synthesized dataset.

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Section: Related Workmentioning

confidence: 99%

A generative model for non-Intrusive load monitoring in commercial buildings

Henriet

Şimşekli

Fuentes³

et al. 2018

Energy and Buildings

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“…As mentioned in [42], one of the key points for designing a MF problem is to find an appropriate application-specific constraint. Unlike previous methods, in this paper, we innovatively integrate the technical idea of the event-based approaches into the MF problem.…”

Section: Graph Shift Quadratic Form Constrained Active Power Disaggrementioning

confidence: 99%

Low-Rate Non-Intrusive Load Disaggregation with Graph Shift Quadratic Form Constraint

Liu

2018

Applied Sciences

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Non-intrusive load monitoring (NILM) is a cost-effective technique for extracting device-level energy consumption information by monitoring the aggregated signal at the entrance of the electric power. With the large-scale deployment of smart metering, NILM should ideally be designed to operate purely on the low-rate data from smart meters. In this paper, an approach based on Graph Shift Quadratic Form constrained Active Power Disaggregation (GSQF-APD) is proposed, which is built upon matrix factorization and introduces graph shift quadratic form constraint according to piecewise smoothness of the power signal. In addition, a two-step iterative optimization method is designed to solve this problem. The first step minimizes the regularization term to find the signal with minimum variation, and then the second step uses the simulated annealing (SA) algorithm to iteratively minimize the objective function and constraint based on the total graph variation minimizer. Using one open-access dataset, the strength of GSQF-APD is demonstrated through three sets of experiments. The numerical results show the superior performance of GSQF-APD, with Graph Laplacian Quadratic Form constrained Active Power Disaggregation (GLQF-APD) and the state-of-the-art NILM methods as benchmarks.

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“…In recent years several studies have been carried out in the context of compact dictionary learning [17][18][19], as an approach for finding a compact representation of the data [20][21][22][23][24][25]. However, the lack of physical interpretation of the compact dictionary (i.e., physical meaning of each basis in the dictionary) has been a critical shortcoming of the standard dictionary learning techniques.…”

Section: Compact Representation Of the Featuresmentioning

confidence: 99%

Feature encoding in band-limited distributed surveillance systems

Rahimpour

Taalimi

2017

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Distributed surveillance systems have become popular in recent years due to security concerns. However, transmitting high dimensional data in bandwidth-limited distributed systems becomes a major challenge. In this paper, we address this issue by proposing a novel probabilistic algorithm based on the divergence between the probability distributions of the visual features in order to reduce their dimensionality and thus save the network bandwidth in distributed wireless smart camera networks. We demonstrate the effectiveness of the proposed approach through extensive experiments on two surveillance recognition tasks.

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Non-Intrusive Energy Disaggregation Using Non-Negative Matrix Factorization With Sum-to-k Constraint

Cited by 106 publications

References 55 publications

A generative model for non-Intrusive load monitoring in commercial buildings

A generative model for non-Intrusive load monitoring in commercial buildings

Low-Rate Non-Intrusive Load Disaggregation with Graph Shift Quadratic Form Constraint

Feature encoding in band-limited distributed surveillance systems

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