The Empty-Nest Power User Management Based on Data Mining Technology
Author:
Li Jing1, Yang Jiahui1, Cai Hui1, Jiang Chi2, Jiang Qun2, Xie Yue1, Lu Zimeng1, Li Lingzhi1, Sun Guanqun3
Affiliation:
1. College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China 2. Electric Power Research Institute, State Grid Zhejiang Electric Power Co., Ltd., Hangzhou 310007, China 3. College of Modern Science and Technology, China Jiliang University, Yiwu 322002, China
Abstract
With the aging of the social population structure, the number of empty-nesters is also increasing. Therefore, it is necessary to manage empty-nesters with data mining technology. This paper proposed an empty-nest power user identification and power consumption management method based on data mining. Firstly, an empty-nest user identification algorithm based on weighted random forest was proposed. Compared with similar algorithms, the results indicate that the performance of the algorithm is the best, and the identification accuracy of empty-nest users is 74.2%. Then a method for analyzing the electricity consumption behavior of empty-nest users based on fusion clustering index adaptive cosine K-means was proposed, which can adaptively select the optimal number of clusters. Compared with similar algorithms, the algorithm has the shortest running time, the smallest Sum of the Squared Error (SSE), and the largest mean distance between clusters (MDC), which are 3.4281 s, 31.6591 and 13.9513, respectively. Finally, an anomaly detection model with an Auto-regressive Integrated Moving Average (ARIMA) algorithm and an isolated forest algorithm was established. The case analysis shows that the recognition accuracy of abnormal electricity consumption for empty-nest users was 86%. The results indicate that the model can effectively detect the abnormal behavior of empty-nest power users and help the power department to better serve empty-nest users.
Funder
Zhejiang Provincial Natural Science Foundation of China
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference29 articles.
1. Future trends of China’s population and aging: 2015~2100;Zhai;Popul. Res.,2017 2. Trends of Population Aging in China and the World as a Whole;Liu;Sci. Res. Aging,2021 3. State Council Information Office (2022, October 26). Transcript of the Regular Press Conference Held by the Ministry of Civil Affairs in the Fourth Quarter of 2022, Available online: http://www.scio.gov.cn/xwfbh/gbwxwfbh/xwfbh/mzb/Document/1732433/1732433.htm. 4. State Council Information Office (2022, September 20). Text Transcript of the Series of Press Conferences (Nineteenth Session) Held by the Health and Medical Commission on “Everything for the People’s Health—Our Ten Years”, Available online: http://www.scio.gov.cn/xwfbh/gbwxwfbh/xwfbh/wsb/Document/1730851/1730851.htm. 5. Chuang, M., Yikuai, W., Junda, Z., Ke, C., Feixiang, G., Tao, C., and Songsong, C. (2021, January 26–28). Research on User Electricity Consumption Behavior and Energy Consumption Modeling in Big Data Environment. Proceedings of the 2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), Nanchang, China.
|
|