Analysis of PV Poverty Alleviation Users Based on Big Data
- DOI
- 10.2991/cnci-19.2019.10How to use a DOI?
- Keywords
- Photovoltaic poverty alleviation, logistic regression algorithm,user label, index system.
- Abstract
Based on the historical data of power customers, the index system needed to determine the model is determined based on the customer's basic attributes, power usage behavior, payment behavior, customer credit, geographic region, and weather environment. Through the correlation coefficient matrix and information value (IV) of the indicator, the index variables that finally enter the model are selected, the variables are grouped by the optimal grouping method and the weight of Evidence (WOE) is transformed. Based on the processed data, the logistic regression algorithm is used to construct the PV poverty alleviation user analysis model, and the users are classified into high, medium and low grade suspected poor users according to the analysis model, and the analysis results are fed back to the government's poverty alleviation office for confirmation feedback. This paper provide a basis for selecting poor households in the Xinjiang Poverty Alleviation Competition.
- 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 - Jian Li PY - 2019/05 DA - 2019/05 TI - Analysis of PV Poverty Alleviation Users Based on Big Data BT - Proceedings of the 2019 International Conference on Computer, Network, Communication and Information Systems (CNCI 2019) PB - Atlantis Press SP - 74 EP - 82 SN - 2352-538X UR - https://doi.org/10.2991/cnci-19.2019.10 DO - 10.2991/cnci-19.2019.10 ID - Li2019/05 ER -