The multi-dimensional power big data mining based on improved grey clustering algorithm

Article type: Research Article

Authors: Li, Hui^{; *} | Lu, Guangqian

Affiliations: Information center of Yunnan Power Grid Corporation, Kunming, 550002, Yunnan, China

Correspondence: [*] Corresponding author. E-mail: huitang@36haojie.com.

Abstract: In order to overcome the problems of the traditional power big data mining methods, such as the low integrity of data mining and the long time-consuming of data mining, this paper realizes multi-dimensional power big data mining by improving the grey clustering algorithm. Firstly, a relay multi hop network is established to collect power big data through the collector; Secondly, Lagrange interpolation method is used to fill the missing data of power data mining; Standardized processing of power consumption data; Finally, according to the grey theory and FCM clustering algorithm, the multi-dimensional power big data mining is realized. The experimental results show that the integrity of power big data mining in this method is up to 0.996, the mining time is not more than 3.05 s, and the mining integrity is up to 0.992, which indicates that this method can effectively improve the effect of power big data mining.

Keywords: Improved grey clustering algorithm, data mining, normalization treatment, objective function, FCM clustering algorithm

DOI: 10.3233/WEB-220048

Journal: Web Intelligence, vol. 21, no. 2, pp. 203-210, 2023