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Abstract

Smart metering data are providing new opportunities for various energy analyses at household level. However traditional load analyses based on time-series techniques are challenged due to the irregular patterns and large volume from smart metering data. This paper proposes a promising alternative to decompose smart metering data in the spectral domain, where i) the irregular load profiles can be characterized by the underlying spectral components, and ii) massive amount of load data can be represented by a small number of coefficients extracted from spectral components. This paper assesses the performances of load characterization at different aggregated levels by two spectral analysis techniques, using the discrete Fourier transform (DFT) and discrete wavelet transform (DWT). Results show that DWT significantly outperforms DFT for individual smart metering data while DFT could be effective at a highly aggregated level.
Original languageEnglish
Article number7779140
Pages (from-to)976-984
JournalIEEE Transactions on Industrial Informatics
Volume13
Issue number3
Early online date9 Dec 2016
DOIs
Publication statusPublished - Jun 2017

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Discrete Fourier transforms
Discrete wavelet transforms
Spectrum analysis
Time series

Cite this

Load Characterization and Low-order Approximation for Smart Metering Data in the Spectral Domain. / Li, Ran; Li, Furong; Smith, Nathan D.

In: IEEE Transactions on Industrial Informatics, Vol. 13, No. 3, 7779140, 06.2017, p. 976-984.

Research output: Contribution to journalArticle

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