Classification model of electricity consumption behavior based on sparse denoising autoencoder feature dimensionality reduction and spectral clustering

• Published in: International journal of electrical power & energy systems Vol. 158; p. 109960
• Main Authors: Huang, Yifan, Yao, Zhengnan, Xu, Qifeng
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.07.2024; Elsevier
• Subjects: Autoencoder; Clustering; Electricity consumption behavior classification; Feature extraction; Secondary classification; Feature extraction; Electricity consumption behavior classification; Secondary classification; Clustering; Autoencoder
• ISSN: 0142-0615; 1879-3517
• DOI: 10.1016/j.ijepes.2024.109960

•A classification model of electricity consumption based on sparse denoising autoencoder and spectral clustering is proposed.•The SDAE and the manually defining electricity consumption characteristic indicators are adopt to improve classification.•The t-SNE is employed to visualize the classification results to achieve the secondary classification.•The Gaussian distance weighting are employed to depict the typical electricity consumption behaviors. The development of electrical measurement technology has brought high latitude residential electricity consumption data to power companies, which contains the characteristics of users' electricity consumption behavior and provides data support for the behavior classification. In order to improve the efficiency of data feature extraction and the accuracy of electricity consumption behavior identification, a classification model based on sparse denoising autoencoder feature dimensionality reduction and spectral clustering is proposed in this paper. Firstly, the sparse denoising autoencoder (SDAE) and the manually defining electricity consumption characteristic indicators are deployed to extract features from the residential daily electricity consumption data, and then the spectral clustering is employed to classify the extracted electricity consumption characteristics. Secondly, the t-distributed stochastic neighbor embedding (t-SNE) is applied to visualize and analyze the classification results, and on this basis, the secondary classification is implemented to fix the issue of the confused electricity consumption behaviors. Finally, the typical consumption behavior curves are calculated by Gaussian distance weighting method, and the characteristics of power consumption behavior are analyzed and summarized. The proposed approach is evaluated and verified by using the electricity dataset in Fujian, China.