Next Article in Journal
Robust Combined Binarization Method of Non-Uniformly Illuminated Document Images for Alphanumerical Character Recognition
Previous Article in Journal
Towards Naples Ecological REsearch for Augmented Observatories (NEREA): The NEREA-Fix Module, a Stand-Alone Platform for Long-Term Deep-Sea Ecosystem Monitoring
Previous Article in Special Issue
A Multi-User, Single-Authentication Protocol for Smart Grid Architectures
Open AccessArticle

Spectral Analysis of Electricity Demand Using Hilbert–Huang Transform

1
Dpto. Tecnología Electrónica, Universidad de Sevilla, Av. Reina Mercedes s/n, 41004 Sevilla, Spain
2
Department of Computer Science, Bioengineering, Robotics and Systems Engineering, University of Genoa, Via Opera Pia 13, I-16145 Genoa, Italy
3
Network Technology and Innovability, Enel Global Infrastructure and Networks, 00198 Rome, Italy
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(10), 2912; https://doi.org/10.3390/s20102912
Received: 20 April 2020 / Revised: 15 May 2020 / Accepted: 19 May 2020 / Published: 21 May 2020
(This article belongs to the Special Issue Sensors and Data Analytic Applications for Smart Grid)
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. View Full-Text
Keywords: Hilbert–Huang Transform; Empirical Mode Decomposition; spectral analysis; electricity demand Hilbert–Huang Transform; Empirical Mode Decomposition; spectral analysis; electricity demand
Show Figures

Figure 1

MDPI and ACS Style

Luque, J.; Anguita, D.; Pérez, F.; Denda, R. Spectral Analysis of Electricity Demand Using Hilbert–Huang Transform. Sensors 2020, 20, 2912.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop