| Issue |
E3S Web Conf.
Volume 53, 2018
2018 3rd International Conference on Advances in Energy and Environment Research (ICAEER 2018)
|
|
|---|---|---|
| Article Number | 02006 | |
| Number of page(s) | 4 | |
| Section | Energy Equipment and Application | |
| DOI | https://doi.org/10.1051/e3sconf/20185302006 | |
| Published online | 14 September 2018 | |
Residential power user segmentation based on k-means clustering method in the context of big data
1
School of Economics and Management, North China Electric Power University, Beijing, 102206, China
2
State Grid Energy Research Institute CO., LTD, Beijing 100052, China
* Corresponding author: 2234923819@qq.com
With the deepening of the reform on the selling side of electricity, the selling company must strengthen the analysis on the electricity consumption of users and arrange the purchase and sale of electricity scheme scientifically so as to occupy the target selling electricity market and obtain the profit of selling electricity. Based on the big data background, the paper uses the k-means clustering method to divide the load curve of 2,498 residential power users into 5 categories. On the premise of considering the system load, the above five categories are classified into three types: peak load, partial peak load, and stable power users.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.