Residential Appliance Identification Based on Spectral Information of Low Frequency Smart Meter Measurements

Dinesh, Chinthaka; Nettasinghe, Buddhika; Godaliyadda, G. M. R. I.; Ekanayake, M. P. B.; Ekanayake, Janaka; Wijayakulasooriya, J. V.

doi:10.1109/tsg.2015.2484258

Cited by 85 publications

(58 citation statements)

References 24 publications

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“…First, each active power signal was split into sliding windows (SWs) of 10 samples. Then from each SW, 5 uncorrelated spectral components which are referred as subspace components (SCs) were extracted using the Karhunen Loeve Expansion (KLE) based method [18,22,37] described in Appendix A. Here, the KLE based spectral feature extraction method was used as unique signature information of active power signals might not be apparent in the time domain profiles due to their low sampling rate.…”

Section: Spectral Features Of Individual Appliancesmentioning

confidence: 99%

“…Here, the KLE based spectral feature extraction method was used as unique signature information of active power signals might not be apparent in the time domain profiles due to their low sampling rate. KLE allows the extraction of hidden features of time domain active power signals by utilizing their uncorrelated spectral components [18,23,38]. This process is shown in Figure3. A given SC is then further decomposed to generate more SCs to improve the resolution.…”

Section: Spectral Features Of Individual Appliancesmentioning

confidence: 99%

“…Due to its both low cost and complexity, numerous NILM methods have been proposed in the literature [11,[17][18][19][20][21][22][23][24]. These methods enable the identification of turned ON appliances inside a customer premises with their respective power levels non-intrusively.…”

Section: Introductionmentioning

confidence: 99%

“…As a remedy to these common drawbacks of existing NILM methods, this paper introduces a novel NILM method which extends the previously proposed NILM method in [18] by the authors. The proposed NILM method relies on a Karhunen Loéve expansion (KLE) based subspace separation method [18,23] which successfully operates even with only active power measurements collected at a low sampling rate (slower than 1 Hz).…”

Section: Introductionmentioning

confidence: 99%

“…The proposed NILM method relies on a Karhunen Loéve expansion (KLE) based subspace separation method [18,23] which successfully operates even with only active power measurements collected at a low sampling rate (slower than 1 Hz).…”

Section: Introductionmentioning

confidence: 99%

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Non-intrusive load monitoring under residential solar power influx

et al. 2017

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This paper proposes a novel Non-Intrusive Load Monitoring (NILM) method for a consumer premises with a residentially installed solar plant. This method simultaneously identifies the amount of solar power influx as well as the turned ON appliances, their operating modes, and power consumption levels. Further, it works effectively with a single active power measurement taken at the total power entry point with a sampling rate of 1 Hz. First, a unique set of appliance and solar signatures were constructed using a high-resolution implementation of Karhunen Loéve expansion (KLE). Then, different operating modes of multi-state appliances were automatically classified utilizing a spectral clustering based method. Finally, using the total power demand profile, through a subspace component power level matching algorithm, the turned ON appliances along with their operating modes and power levels as well as the solar influx amount were found at each time point. The proposed NILM method was first successfully validated on six synthetically generated houses (with solar units) using real household data taken from the Reference Energy Disaggregation Dataset (REDD) -USA. Then, in order to demonstrate the scalability of the proposed NILM method, it was employed on a set of 400 individual households. From that, reliable estimations were obtained for the total residential solar generation and for the total load that can be shed to provide reserve services. Finally, through a developed prediction technique, NILM results observed from 400 households during four days in the recent past were utilized to predict the next day's total load that can be shed. * Corresponding AuthorEmail addresses: hchintha@sfu.ca; dineshhgcp@gmail.com (Chinthaka Dinesh), shirantha.welikala.2015@IEEE.org; shirantha27@gmail.com (Shirantha Welikala), yasithashehanliyanage@gmail.com; wlysliyanage@IEEE.org (Yasitha Liyanage), mpb.ekanayake@ee.pdn.ac.lk (Mervyn Parakrama B. Ekanayake), roshangodd@ee.pdn.ac.lk (Roshan Indika Godaliyadda), jbe@ee.pdn.ac.lk; EkanayakeJ@cardiff.ac.uk (Janaka Ekanayake) Proposed

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Section: Spectral Features Of Individual Appliancesmentioning

confidence: 99%

Section: Spectral Features Of Individual Appliancesmentioning

confidence: 99%