Abstract
The big data of electric power industry contains the information of users’ values, credits, and behavior preferences. On the basis of the data, electric power companies can provide personal services as well as increasing profits. In this paper, we proposed a labeling system based on the clustering algorithms and Gradient Boost Decision Tree (GBDT) algorithm to establish user behavior profiles for State Grid Group of China, including basic information labels, behavior labels, behavior description labels, behavior prediction, and user classification. The experimental results showed that the approach can describe the behavior features of users in the electric power industry effectively.
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Song, W., Liu, G., Luo, D., Gao, D. (2018). User Behavior Profiles Establishment in Electric Power Industry. In: Xhafa, F., Patnaik, S., Zomaya, A. (eds) Advances in Intelligent Systems and Interactive Applications. IISA 2017. Advances in Intelligent Systems and Computing, vol 686. Springer, Cham. https://doi.org/10.1007/978-3-319-69096-4_2
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DOI: https://doi.org/10.1007/978-3-319-69096-4_2
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