Design of an Intelligent Processing System for Business Data Analysis Based on Improved Clustering Algorithm

Wang, Ning

doi:10.1016/j.procs.2023.11.105

Cited by 1 publication

(1 citation statement)

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“…The underlying clustering algorithm tends to randomly select the clustering centre, which makes it sensitive to the initial value and easy to fall into the local optimum in the clustering process [8] . In this paper, an artificial bee colony algorithm improved k-means clustering algorithm [9] is used for clustering analysis of the user's electricity consumption behaviour, and a more optimal clustering centre is selected by calculating the fitness value of the nectar source, in order to achieve the update of the clustering results and to improve the convergence speed and accuracy of the clustering process.The algorithm process is shown in Figure 1:…”

Section: Improved K-means Clustering Algorithmmentioning

confidence: 99%

Research on multi-type demand response user profile based on improved k-means clustering algorithm

Jiang,

qi,

Zhang

et al. 2024

Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023)

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The customer power load has the characteristics of complex and irregular use. Therefore, in order to meet the demand response scheduling requirements, cluster analysis should be carried out according to the user load characteristics. To solve the above problems, this paper proposes a multi-type demand response user portrait research method based on improved k-means clustering algorithm. Firstly, the improved k-means clustering algorithm is used to analyze the user's electricity consumption data, study the user's electricity consumption behavior, and classify the user. Then, considering the dynamic elastic adjustment coefficient, the price-based demand response model is constructed. Finally, select the demand response characteristic index, construct the demand response characteristic evaluation system and establish the user demand response portrait. The validity of the model is verified by analyzing the energy use data of a park in Shandong Province. The example analysis shows that the method used in this paper can effectively reflect the acceptability of demand response of different types of users under different electricity prices.

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Section: Improved K-means Clustering Algorithmmentioning

confidence: 99%