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Prediction of Rolling Force Based on a Fusion of Extreme Learning Machine and Self Learning Model of Rolling Force Read chapter Read first chapter

2018 | OriginalPaper | Chapter

User Behavior Profiles Establishment in Electric Power Industry

Authors : Wei Song, Gang Liu, Di Luo, Di Gao

Published in: Advances in Intelligent Systems and Interactive Applications

Publisher: Springer International Publishing

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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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Metadata
Title
User Behavior Profiles Establishment in Electric Power Industry
Authors
Wei Song
Gang Liu
Di Luo
Di Gao
Copyright Year
2018
Book
Advances in Intelligent Systems and Interactive Applications

Print ISBN: 978-3-319-69095-7

Electronic ISBN: 978-3-319-69096-4

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-69096-4_2

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