Mining potential customers of electric power industry based on customer behavior analysis

Xinlei Huang; Yongjun Feng; Hailong Wang; Jiande Xue; Panpan Li

doi:10.1117/12.2673071

25 April 2023 Mining potential customers of electric power industry based on customer behavior analysis

Xinlei Huang, Yongjun Feng, Hailong Wang, Jiande Xue, Panpan Li

Proceedings Volume 12598, Eighth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2022); 125982I (2023) https://doi.org/10.1117/12.2673071
Event: Eighth International Conference on Energy Materials and Electrical Engineering (ICMEE 2022), 2022, Guangzhou, China

Abstract

With the continuous development of China's electricity market, the characteristics of diversification of electricity market demand are gradually highlighted. How to optimize the marketing strategy to meet the electricity demand of different customers and improve the core competitiveness has become an urgent requirement of electric power enterprises. Aiming at the problem of long mining time when using traditional mining methods to mine suitable potential customers in the power industry, a mining method of potential customers in the power industry based on customer behavior analysis is designed. Firstly, some data of potential customers in power industry are extracted and preprocessed. Then, the customer behavior analysis method is used to extract the characteristics of customer behavior. Finally, the mining model of potential customers in the power industry is constructed to realize the mining of potential customers. The experimental results show that the potential customer mining method based on customer behavior analysis is faster than the traditional method, which provides a feasible research direction for the expansion of the potential customer market in the power industry and has practical research value.

Citation Download Citation

Xinlei Huang, Yongjun Feng, Hailong Wang, Jiande Xue, and Panpan Li "Mining potential customers of electric power industry based on customer behavior analysis", Proc. SPIE 12598, Eighth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2022), 125982I (25 April 2023); https://doi.org/10.1117/12.2673071

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KEYWORDS

Mining

Data modeling

Industry

Feature extraction

Data processing

Analytical research

Power consumption

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