Wavelet Decomposition and Singular Spectrum Analysis for electrical signal denoising

The aggregated electrical load of a household network contains relevant information. Specifically, which loads are related to electrical appliances switched on. However, when real-world data is at stake, not only this specific data must be individually recognized, but also there is other non-relevant information that can be thought as noise in the electrical signal. Therefore, to extract the important information we need to use signal denoising algorithms. This work presents a comparison for the application of an algorithm based on Wavelet Decomposition versus the Singular Spectrum Analysis to the denoise of aggregated electrical signal. These techniques were applied both in an artificially generated signal as well as to the analysis of a signal obtained from an ordinary household. For the latter, the experiments highlighted a small set of wavelet functions that are more suitable for the problem being tackled. Finally, the comparison of the performance of each of the approaches applied to the same sampled signal data indicate the effectiveness of both denoising techniques for use over real data sets.