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Load Characterization and Low-order Approximation for Smart Metering Data in the Spectral Domain


Reference:

Li, R., Li, F. and Smith, N. D., 2017. Load Characterization and Low-order Approximation for Smart Metering Data in the Spectral Domain. IEEE Transactions on Industrial Informatics, 13 (3), 7779140.

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Official URL:

https://doi.org/10.1109/TII.2016.2638319

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.

Details

Item Type Articles
CreatorsLi, R., Li, F. and Smith, N. D.
DOI10.1109/TII.2016.2638319
DepartmentsFaculty of Engineering & Design > Electronic & Electrical Engineering
Research CentresCentre for Sustainable Power Distribution
EPSRC Centre for Doctoral Training in Statistical Mathematics (SAMBa)
Centre for Doctoral Training in Decarbonisation of the Built Envinronment (dCarb)
RefereedYes
StatusPublished
ID Code54547

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