Smart home: A novel model for denoising an electrical signal

Emerging trends for energy monitoring as in Smart Energy Systems require intelligent solutions for appliances identification. Non-intrusive load monitoring (NILM) systems are able to extract particular features from the aggregate consumption of the electrical network. However, the whole-consumption signal is contaminated with noise, which hinders successful load disambiguation of individual appliances. In this work, we propose a novel approach to denoise a signal based on the techniques of Embedding, Wavelet Shrinkage and Diagonal Averaging. The embedding stage transforms the one-dimensional signal into a sequence of lagged vectors. These vectors are denoised using wavelet decomposition. Finally, the denoised signal is obtained by taking the diagonal averages of the resultant matrix. Our approach is compared to Wavelet Decomposition and Singular Spectrum Analysis methods for electrical signal denoising. The results are very favorable since they yield better performance as highlighted by the statistical tests performed.