Customer Identification of Potential Energy Substitution Based on Big Data Method
- DOI
- 10.2991/cnci-19.2019.9How to use a DOI?
- Keywords
- Electrical energy substitution, Logistic regression algorithm, Score-card algorithm.
- Abstract
The usage of clean energy such as electric energy will help promote energy conservation and emission reduction, optimize energy use structure and improve energy efficiency. The realization of the "electric energy substitution" strategy can realize the replacement of loose coal and direct fuel in the terminal energy consumption, and finally realize the fundamental transformation of the energy development mode. This paper analyzed the customer characteristics of electric energy substitution potential from multiple dimensions, built a multi-dimensional linear electric energy substitution evaluation prediction model based on logistic regression algorithm, and quantified the output customer comprehensive score based on the obtained model results to identify high potential customers. At the same time, from the perspectives of government, power grid and enterprise, we utilized big data mining technology to analyze characteristics of high-potential customers, tapped customer demand characteristics, and achieved precise services.
- Copyright
- © 2019, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Yunzhao Li PY - 2019/05 DA - 2019/05 TI - Customer Identification of Potential Energy Substitution Based on Big Data Method BT - Proceedings of the 2019 International Conference on Computer, Network, Communication and Information Systems (CNCI 2019) PB - Atlantis Press SP - 63 EP - 73 SN - 2352-538X UR - https://doi.org/10.2991/cnci-19.2019.9 DO - 10.2991/cnci-19.2019.9 ID - Li2019/05 ER -