Enterprise Credit Rating Model Based on Long and Short-Term Trend of Desensitized Power Load Data
Authors: 
Yi Wu, Zenghui Xi, Weibin Wang, Qian LiAuthors Info & Claims
Yi Wu, Zenghui Xi, Weibin Wang, Qian LiAuthors Info & ClaimsPages 1060 - 1064
Abstract
This paper explores the fusion and application of big data mining and artificial intelligence technology to delve into the value of power data assets. It advances the research on the business model of power data in enterprise credit and explores the feasibility of implementing power data assets. Based on the exploration of business models and existing project databases, the paper, considering the characteristics of the power industry and data foundation, uses data mining methods, label system construction methods, and credit evaluation system methods to construct an attentional convolution neural network-based credit rating (ACNNCR) model for power big data. Utilizing clustering algorithms, expert rules, statistical modelling, mining algorithms, etc., the paper develops a set of power credit label models, including factual labels, rule-based labels, and predictive labels, within dimensions such as user attributes, electricity usage characteristics, and credit features, based on non-residential user data. A total of 185 features are successfully established for attentional convolution neural network model, forming a new type of power data asset. Case verification on real-life datasets is used to validate the effectiveness of the constructed power credit labels.
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- Enterprise Credit Rating Model Based on Long and Short-Term Trend of Desensitized Power Load Data
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Published In
November 2023
1156 pages
ISBN:9798400716478
DOI:10.1145/3656766
Copyright © 2023 ACM.
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Association for Computing Machinery
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Publication History
Published: 01 June 2024
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- Research-article
- Research
- Refereed limited
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- y Science and Technology Project of State Grid Shanghai Municipal Electric Power Company
Conference
ICBAR 2023
ICBAR 2023: 2023 3rd International Conference on Big Data, Artificial Intelligence and Risk Management
November 24 - 26, 2023
Chengdu, China
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