State of art on load monitoring methods

Najmeddine, Hala; Drissi, Khalil El Khamlichi; Pasquier, Christophe; Faure, Claire; Kerroum, Kamal; Diop, Alioune; Jouannet, Thierry; Michou, Michel

doi:10.1109/pecon.2008.4762669

Cited by 74 publications

(48 citation statements)

References 4 publications

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“…Real and reactive power are the most commonly used steady-state characteristics in NILM systems. In works such as [18] and [14], it is assumed that the real power is used to disaggregate the devices, having results with great precision. The transient behavior of most electrical appliances is distinct, therefore, this feature becomes suitable for the identification of load [19].…”

Section: Non-intrusive Load Monitoring Systemsmentioning

confidence: 99%

Home Appliance Identification for Nilm Systems Based on Deep Neural Networks

Penha¹,

Castro²

2018

IJAIA

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Section: Non-intrusive Load Monitoring Systemsmentioning

confidence: 99%

Home Appliance Identification for Nilm Systems Based on Deep Neural Networks

Penha¹,

Castro²

2018

IJAIA

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“…There are many signatures of the load, mainly including transient state and steady state characteristics [13] . Transient state process includes more useful information than steady state, and easier to identify, because the transient state characteristics have a close relationship with their physical properties and missions.…”

Section: Transient Waveformmentioning

confidence: 99%

Classified Measurement of Household Load for Smart Meter

Xu¹,

Gao²,

Dai³

et al. 2013

Proceedings of the 2013 International Conference on Advanced Computer Science and Electronics Information

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-The classified measurement of household load can make home users keep abreast of the electricity consumption in their home and guide them use electricity reasonably, and at the same time strengthen the demand side management (DSM). Load identification is the base of the classified measurement of household load. According to the shortcomings of the existing load identification which often use the steady state characteristics as the signature, and considering the uniqueness of the transient characteristics of household load, a method was proposed to recognize the time it turns on or off, and then measure the electricity of each load based on Minkowski distance used transient waveform of the active power and reactive power as the signature. Firstly, set up a template library as the comparative standard to recognize. And then calculate the matching degree between the waveform acquired and in template library to determine at the moments which load turns on or off or no load changes its state. Afterwards the time each load used is obtained. Finally, on the basis of the use of time of each load, calculate its power consumption. The process of the method is simple, and the result by measured data has proved the method has high accuracy and feasibility. It can be used for classified measurement of household load for smart meter.

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“…This brings about increases in overall electricity consumption in urban residential areas because of increased air conditioner (AC) usage under hot weather conditions. Over the past decades, many efforts have been made to monitor global energy consumption [3]- [5]. For example, researchers studied the impact of household characteristics on electricity consumption for 27 households in Ireland over 20 months [6].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Measurements of Household Electricity Demand During Hot Weather in Kuala Lumpur

Ranjbar

Zaki

Yusoff

et al. 2017

IJECE

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The aim of this study was to conduct short-term measurements on household electricity demand under hot weather conditions in a residential area in Kuala Lumpur. The measurements included total and air conditioner (AC) electricity consumption of 10 households in an apartment building as well as outdoor air temperatures, which were collected from March to May 2016. Results indicated that the average AC electricity consumption contributed to a major portion of total household electricity consumption, which ranged from 19.4 to 52.3% during the measurement period. Additionally, 1-minute interval time series data indicated household energy consumption more accurately than 30- or 60-minute interval.

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State of art on load monitoring methods

Cited by 74 publications

References 4 publications

Home Appliance Identification for Nilm Systems Based on Deep Neural Networks

Home Appliance Identification for Nilm Systems Based on Deep Neural Networks

Classified Measurement of Household Load for Smart Meter

Short-Term Measurements of Household Electricity Demand During Hot Weather in Kuala Lumpur

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