ArticleSensors (Basel, Switzerland)2020
Spectral Analysis of Electricity Demand Using Hilbert-Huang Transform.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Health Monitoring via Heart, Breath, and Korotkoff Sounds by Wearable Piezoelectret Patches.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Mixed-Input Deep Learning Approach to Sleep/Wake State Classification by Using EEG Signals.Diagnostics (Basel, Switzerland) · 2023Article
- Time-trend analysis of the center frequency of the intrinsic mode function from the Hilbert-Huang transform of electroencephalography during general anesthesia: a retrospective observational study.BMC anesthesiology · 2023Observational
- Grid Frequency Measurement through a PLHR Analysis Obtained from an ELF Magnetometer.Sensors (Basel, Switzerland) · 2022Article
- Multilevel Fine Fault Diagnosis Method for Motors Based on Feature Extraction of Fractional Fourier Transform.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
4 authors.
Funding
Abstract
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.
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