Affiliation:
1. School of Control and Computer Engineering, North China Electric Power University, 2 Beinong Road, Huilongguan
Town, Changping District, Beijing, 102206, China
Abstract
Background:
With the further opening of the electricity sales market, based on the current
power reform situation, it is more emphasized to focus on the market and customers and carry
out value marketing. Therefore, further mining the user value has become a necessary means for the
transformation of power grid enterprises, and the construction of power user behavior portraits has
very practical significance for the business expansion of power grid companies and the improvement
of customer service levels.
Objective:
In order to further explore user value, a method of user electricity behavior portrait based
on QFPAK-means (quantum flower pollination K-means) clustering is proposed.
Methods:
Through the quantum flower pollination algorithm, considering the overhead cost, the
optimal classification number is automatically determined, and on this basis, K-means clustering is
completed. In the meantime, the typical power consumption patterns of users are extracted by using
the K-means clustering algorithm based on quantum flower pollination, and the features are extracted
as the power consumption behavior portraits of users.
Results:
Through the comparative simulation of different methods, the effectiveness of the proposed
algorithm is verified, which can provide outstanding guidance for power grid companies to expand
their business and improve customer service levels.
Conclusion:
A method of user electricity behavior portrait based on QFPAK-means clustering is
proposed to further explore user value, and the experimental results demonstrate the effectiveness
and advantage of the proposed method.
Publisher
Bentham Science Publishers Ltd.
Subject
Electrical and Electronic Engineering,Electronic, Optical and Magnetic Materials