Adaptive construction method of high-quality electricity user portrait based on intuitionistic fuzzy set
How to accurately describe the characteristics of users' electricity consumption and build a comprehensive portrait of high-quality users has become the key to improve the quality of power service and operational efficiency. However, traditional user portrait construction methods are often based on fixed classification standards, and it is difficult to accurately capture the diversity of users' electricity consumption behavior. Therefore, this paper proposes an adaptive construction method of high-quality power users' portraits based on intuitionistic fuzzy sets. Calculate that frequency of users connecte to the same network, and extracting the characteristics of high-quality electricity users' portrait in all directions in space and in the whole process in time to obtain an extraction result; Calculating an intuitionistic fuzzy set, further screening the data of high-quality users and general users on the basis of the set, and generating a portrait label of high-quality power users; This paper analyzes the characteristics of electricity consumption behavior, credit status and demand for value-added services, and adaptively constructs a portrait of high-quality electricity users. The experimental results show that the self-adaptive construction of high-quality electricity users' portraits obtained by this method has higher satisfaction and the maximum confidence is on the rise, and the self-adaptive construction results of high-quality electricity users' portraits are better.
Authors
- Kaihua Jiang (ORCID: https://orcid.org/0000-0001-7125-5535)
- Qing Tian (ORCID: https://orcid.org/0000-0003-0030-3645)
- Hao Cui (ORCID: https://orcid.org/0000-0002-9417-5068)
- Yunpeng Liu (ORCID: https://orcid.org/0000-0002-7942-2112)
- Jingyuan Chen (ORCID: https://orcid.org/0000-0003-4748-1182)
Publication Details
- Published
- 2024-12-27
- DOI
- https://doi.org/10.1109/ic2ecs64405.2024.10927729
- Primary Topic
- Digital Media and Visual Art
- Type
- article
- Field-Weighted Citation Impact
- 0.00