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.
Graphical Abstract
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