Spectral Analysis of Electricity Demand Using Hilbert–Huang Transform

Sensors

Davide Anguita¹  Joaquin Luque²  Francisco Pérez²  Robert Denda³ 

[1] Department of Computer Science, Bioengineering, Robotics and Systems Engineering, University of Genoa, Via Opera Pia 13, I-16145 Genoa, Italy;Dpto. Tecnología Electrónica, Universidad de Sevilla, Av. Reina Mercedes s/n, 41004 Sevilla, Spain;Network Technology and Innovability, Enel Global Infrastructure and Networks, 00198 Rome, Italy;

关键词: Hilbert–Huang Transform;    Empirical Mode Decomposition;    spectral analysis;    electricity demand;   

DOI  :  10.3390/s20102912

来源: DOAJ

【 摘 要 】

The large amount of sensors in modern electrical networks poses a serious challenge in the data processing side. For many years, spectral analysis has been one of the most used approaches to extract physically meaningful information from a sea of data. Fourier Transform (FT) and Wavelet Transform (WT) are by far the most employed tools in this analysis. In this paper we explore the alternative use of Hilbert–Huang Transform (HHT) for electricity demand spectral representation. A sequence of hourly consumptions, spanning 40 months of electrical demand in Spain, has been used as dataset. First, by Empirical Mode Decomposition (EMD), the sequence has been time-represented as an ensemble of 13 Intrinsic Mode Functions (IMFs). Later on, by applying Hilbert Transform (HT) to every IMF, an HHT spectrum has been obtained. Results show smoother spectra with more defined shapes and an excellent frequency resolution. EMD also fosters a deeper analysis of abnormal electricity demand at different timescales. Additionally, EMD permits information compression, which becomes very significant for lossless sequence representation. A 35% reduction has been obtained for the electricity demand sequence. On the negative side, HHT demands more computer resources than conventional spectral analysis techniques.

【 授权许可】

Unknown