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Recent Advances in Electrical & Electronic Engineering

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ISSN (Print): 2352-0965
ISSN (Online): 2352-0973

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Review Article

User Power Behavior Similarity Clustering Based on Unsupervised Extreme Learning Machine Algorithm

Author(s): Yuancheng Li*, Yaqi Cui and Xiaolong Zhang

Volume 13, Issue 5, 2020

Page: [641 - 649] Pages: 9

DOI: 10.2174/2352096512666191004130655

Price: $65

Abstract

Background: Advanced Metering Infrastructure (AMI) for the smart grid is growing rapidly which results in the exponential growth of data collected and transmitted in the device. By clustering this data, it can give the electricity company a better understanding of the personalized and differentiated needs of the user.

Objective: The existing clustering algorithms for processing data generally have some problems, such as insufficient data utilization, high computational complexity and low accuracy of behavior recognition.

Methods: In order to improve the clustering accuracy, this paper proposes a new clustering method based on the electrical behavior of the user. Starting with the analysis of user load characteristics, the user electricity data samples were constructed. The daily load characteristic curve was extracted through improved extreme learning machine clustering algorithm and effective index criteria. Moreover, clustering analysis was carried out for different users from industrial areas, commercial areas and residential areas. The improved extreme learning machine algorithm, also called Unsupervised Extreme Learning Machine (US-ELM), is an extension and improvement of the original Extreme Learning Machine (ELM), which realizes the unsupervised clustering task on the basis of the original ELM.

Results: Four different data sets have been experimented and compared with other commonly used clustering algorithms by MATLAB programming. The experimental results show that the US-ELM algorithm has higher accuracy in processing power data.

Conclusion: The unsupervised ELM algorithm can greatly reduce the time consumption and improve the effectiveness of clustering.

Keywords: Smart grid, residential electricity consumption behavior, similarity clustering, feature reduction, load characteristics, US-ELM algorithm.

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