One-millisecond low-cost synchronization of wireless sensor network

Linhares, Robson R.; Lima, Carlos R. Erig; Renaux, Douglas P. B.; Pöttker, Fabiana; Oroski, Elder; Lazzaretti, André E.; Mulinari, Bruna Machado; Ancelmo, Hellen Cristina; Gamba, Adriano; Bernardi, Luiz A.; Lima, Lucas Tokarski

doi:10.1109/sbesc49506.2019.9046070

Cited by 5 publications

(1 citation statement)

References 15 publications

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“…The work presented in this paper tries to focus on the performances rather than costs, to increase calculus accuracy and system capacity, therefore using a powerful embedded system to host the algorithm for a NILM module. This is small part of the project presented in [5], [23]- [25], where the intentions are to use several load monitoring modules units controlled by an Operation Center, whose task is managing each unit and analyze power-consuming from the data collected.…”

Section: Related Workmentioning

confidence: 99%

An Embedded System for NILM Using Machine Learning

Braglia¹,

Lazzaretti²

2021

Anais Do 15. Congresso Brasileiro De Inteligência Computacional

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The interest in power managing systems has been growing in recent years since every industrial or domestic plant moves towards techniques to efficiently reduce energy demand and costs related to it. An attractive solution is represented by Non-Intrusive Load Monitoring (NILM) systems, whose primary purpose is to find a more appropriate way of keeping track of the power consumption caused by each of the loads that are connected to the monitored plant. A possible real-life implementation of a NILM system is addressed in this work, discussing all the fundamental blocks in its structure, including detecting events, feature extraction, and load classification, using publicly available datasets. Additionally, we provide a solution for an embedded system, able to analyze aggregated waveforms and to recognize each appliance’s contribution in it. The main algorithm, its features, drawbacks, and implementation are thus explained, showing current and future challenges for the final application.

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Section: Related Workmentioning

confidence: 99%

An Embedded System for NILM Using Machine Learning

Braglia¹,

Lazzaretti²

2021

Anais Do 15. Congresso Brasileiro De Inteligência Computacional

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Feature Extraction of V–I Trajectory Using 2-D Fourier Series for Electrical Load Classification

et al. 2022

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A Multi-Agent NILM Architecture for Event Detection and Load Classification

et al. 2020

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A multi-agent architecture for a Non-Intrusive Load Monitoring (NILM) solution is presented and evaluated. The underlying rationale for such an architecture is that each agent (load event detection, feature extraction, and classification) outperforms others of the same type in particular scenarios; hence, by combining the expertise of these agents, the system presents an improved performance. Known NILM algorithms, as well as new algorithms, proposed by the authors, were individually evaluated and compared. The proposed architecture considers a NILM system composed of Load Monitoring Modules (LMM) that report to a Center of Operations, required in larger facilities. For the purposed of evaluating and comparing performance, five load event detect agents, five feature extraction agents, and five classification agents were studied so that the best combinations of agents could be implemented in LMMs. To evaluate the proposed system, the COOLL and the LIT-Dataset were used. Performance improvements were detected in all scenarios, with power-ON and power-OFF detection improving up to 13%, while classification accuracy improved up to 9.4%.

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One-millisecond low-cost synchronization of wireless sensor network

Cited by 5 publications

References 15 publications

An Embedded System for NILM Using Machine Learning

An Embedded System for NILM Using Machine Learning

Feature Extraction of V–I Trajectory Using 2-D Fourier Series for Electrical Load Classification

A Multi-Agent NILM Architecture for Event Detection and Load Classification

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