Spectral Analysis of Electricity Demand Using Hilbert-Huang Transform

Area of Science:

• Electrical Engineering
• Signal Processing
• Data Analysis

Background:

• Modern electrical networks generate vast amounts of sensor data, necessitating efficient processing techniques.
• Spectral analysis, using Fourier Transform (FT) and Wavelet Transform (WT), is a common method for extracting insights from this data.
• Traditional spectral analysis methods face challenges in handling the complexity and volume of modern electrical network data.

Purpose of the Study:

• To explore the Hilbert-Huang Transform (HHT) as an alternative spectral analysis technique for electricity demand data.
• To evaluate the effectiveness of HHT in representing and analyzing electrical consumption patterns.
• To compare HHT with conventional spectral analysis methods like FT and WT.

Main Methods:

• Utilized a dataset of hourly electricity consumption in Spain over 40 months.
• Applied Empirical Mode Decomposition (EMD) to decompose the consumption sequence into Intrinsic Mode Functions (IMFs).
• Applied Hilbert Transform (HT) to each IMF to obtain the HHT spectrum.

Main Results:

• HHT produced smoother spectra with more defined shapes and excellent frequency resolution.
• Empirical Mode Decomposition (EMD) facilitated analysis of abnormal electricity demand across different timescales.
• EMD enabled significant information compression, achieving a 35% reduction for the electricity demand sequence with lossless representation.

Conclusions:

• HHT is a promising technique for spectral representation of electricity demand, offering advantages in resolution and analysis depth.
• EMD, as a component of HHT, enhances the understanding of demand patterns and allows for data compression.
• While HHT requires more computational resources, its benefits in data analysis and representation are substantial for electrical networks.