Characterization of household-consumption load profiles in the time and frequency domain

Smart meter (SM) deployment in the residential context provides a vast amount of data that allows diagnose the behavior of household inhabitants. However, the conventional methods to analyze household-consumption load profiles based on time-series techniques, such as Fourier and Wavelet transform, have problems with nonlinear and nonstationary processes. This paper presents a methodology to perform a comprehensive analysis of consumption load profile features based on the detection of oscillation modes in the time–frequency domain for off-line systems. The methodology is based on the Hilbert–Huang transform (HHT) that evaluates the instantaneous frequency (IF) using the empirical mode decomposition (EMD) and some of the variants of this standard technique such as ensemble empirical mode decomposition (EEMD) and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). The study presented in this paper compares the accuracy of different techniques by conducting a case study of two households in Spain. Nonetheless, there is a trade-off between the computational burden and accuracy. Furthermore, the methodology reveals the importance of the data sampling frequency (temporal data granularity) for accurate characterization of the load profile. Some household loads produced oscillation modes that became increasingly ill-suited for resolutions of 1 min or higher.