Electricity Pattern Analysis by Clustering Domestic Load Profiles Using Discrete Wavelet Transform

Senfeng Cen, Jae Hung Yoo and Chang Gyoon Lim
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Senfeng Cen: Department of Computer Engineering, Chonnam National University, Yeosu 59626, Korea
Jae Hung Yoo: Department of Computer Engineering, Chonnam National University, Yeosu 59626, Korea
Chang Gyoon Lim: Department of Computer Engineering, Chonnam National University, Yeosu 59626, Korea

Energies, 2022, vol. 15, issue 4, 1-18

Abstract: Energy demand has grown explosively in recent years, leading to increased attention of energy efficiency (EE) research. Demand response (DR) programs were designed to help power management entities meet energy balance and change end-user electricity usage. Advanced real-time meters (RTM) collect a large amount of fine-granular electric consumption data, which contain valuable information. Understanding the energy consumption patterns for different end users can support demand side management (DSM). This study proposed clustering algorithms to segment consumers and obtain the representative load patterns based on diurnal load profiles. First, the proposed method uses discrete wavelet transform (DWT) to extract features from daily electricity consumption data. Second, the extracted features are reconstructed using a statistical method, combined with Pearson’s correlation coefficient and principal component analysis (PCA) for dimensionality reduction. Lastly, three clustering algorithms are employed to segment daily load curves and select the most appropriate algorithm. We experimented our method on the Manhattan dataset and the results indicated that clustering algorithms, combined with discrete wavelet transform, improve the clustering performance. Additionally, we discussed the clustering result and load pattern analysis of the dataset with respect to the electricity pattern.

Keywords: demand response; discrete wavelet transform; Pearson’s correlation coefficient; principal component analysis; clustering (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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