Abstract
| Original language | English |
|---|---|
| Article number | 7779140 |
| Pages (from-to) | 976-984 |
| Journal | IEEE Transactions on Industrial Informatics |
| Volume | 13 |
| Issue number | 3 |
| Early online date | 9 Dec 2016 |
| DOIs | |
| Publication status | Published - Jun 2017 |
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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 journal › Article
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TY - JOUR
T1 - Load Characterization and Low-order Approximation for Smart Metering Data in the Spectral Domain
AU - Li, Ran
AU - Li, Furong
AU - Smith, Nathan D
PY - 2017/6
Y1 - 2017/6
N2 - 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.
AB - 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.
UR - https://doi.org/10.1109/TII.2016.2638319
U2 - 10.1109/TII.2016.2638319
DO - 10.1109/TII.2016.2638319
M3 - Article
VL - 13
SP - 976
EP - 984
JO - IEEE Transactions on Industrial Informatics
JF - IEEE Transactions on Industrial Informatics
SN - 1551-3203
IS - 3
M1 - 7779140
ER -