An Event Window Based Load Monitoring Technique for Smart Meters

Dong, Ming; Meira, Paulo C. M.; Xu, Wilsun; Freitas, Walmir

doi:10.1109/tsg.2012.2185522

Cited by 129 publications

(58 citation statements)

References 13 publications

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“…2. The digital energy outcomes provide useful information to assess the characteristics (e.g, the possible regularity) of the metered processes, which can be used to identify the "signature" of the processes [26]. a) "large" δ1DE threshold (2000 W) and δ2DE threshold 1600Ws.…”

Section: Resultsmentioning

confidence: 99%

Exploiting event-driven metering to enable enhanced knowledge of electrical energy uses

Simonov

Chicco

Zanetto³

2015

2015 IEEE 15th International Conference on Environment and Electrical Engineering (EEEIC)

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This paper presents the results obtained from a new type of electricity meter, constructed as the industrial application of event-driven metering concepts. The underlying concepts of event-driven metering are applied to gather information on the electrical load patterns. The data formats used are presented and the details of the knowledge representation referring to the results coming from event-driven metering are discussed. The event-driven metering approach is illustrated by using data gathered from real-life applications.

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Section: Resultsmentioning

confidence: 99%

Exploiting event-driven metering to enable enhanced knowledge of electrical energy uses

Simonov

Chicco

Zanetto³

2015

2015 IEEE 15th International Conference on Environment and Electrical Engineering (EEEIC)

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“…Now utilities are massively installing communicating sensors and smart meters for the real time monitoring, controlling, management and planning of the system. The data from the smart grid are used by demand response(DR) programs, distribution grid monitoring system, outage management system, renewable energy planning systems, conservation voltage reduction (CVR) programs, strategic asset management systems, predictive analysis, billing system, load management system [4,5]. Now utilities are facing big challenge from data generated by smart grid as this data is classified as big data.…”

Section: Introductionmentioning

confidence: 99%

Big Data Analytics for Distribution System Monitoring in Smart Grid

Joseph¹,

Jasmin²

2017

IJSH

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“…Non-Intrusive Load Monitoring (NILM) attempts to identify the turned-on appliances from the power supply entry point without attaching any sensors to each individual appliance. The necessity for effective and efficient NILM methods for residential appliance identification has recently escalated due to its application potential for smart grids [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Residential appliance monitoring based on low frequency smart meter measurements

Dinesh

Perera

Godaliyadda

et al. 2015

2015 IEEE International Conference on Smart Grid Communications (SmartGridComm)

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A Non-Intrusive Load Monitoring (NILM) method for residential appliances based on uncorrelated spectral components of an active power signal is presented. This method utilizes the Karhunen Loéve (KL) expansion to breakdown the active power signal into subspace components so as to construct a unique information rich appliance signature. Unlike existing NILM techniques that rely on multiple measurements at high sampling rates, this method works effectively with a single active power measurement taken at a low sampling rate. After constructing the signature data base, subspace component level power conditions were introduced to reduce the number of possible appliance combinations. Then, an algorithm was presented to identify the turned on appliance combination in a given time window. After identifying the turned on appliance combination, another algorithm was introduced to disaggregate the energy contribution of each individual appliance. The case study conducted using tracebase public data set demonstrate the ability of the proposed method to accurately identify and disaggregate individual energy contributions of turned on appliance combinations that contain single state, multi state and continuous varying appliances. Finally, the proposed method was modified to accommodate usage behavior patterns of each residence adaptively. The modification was validated using six US households in REDD public database. This significantly improves the convergence speed of the turned on appliances identification process.

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An Event Window Based Load Monitoring Technique for Smart Meters

Cited by 129 publications

References 13 publications

Exploiting event-driven metering to enable enhanced knowledge of electrical energy uses

Exploiting event-driven metering to enable enhanced knowledge of electrical energy uses

Big Data Analytics for Distribution System Monitoring in Smart Grid

Residential appliance monitoring based on low frequency smart meter measurements

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