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.
