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 1) the irregular load profiles can be characterized by the underlying spectral components, and 2) 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.
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