Study on the label generation model of power suppliers’ bad behavior based on supervised machine learning

Mengru Shen; Hongchun Zhang; Te Pi; Xinkai Wang; Nan Xu

doi:10.1117/12.3023914

9 April 2024 Study on the label generation model of power suppliers’ bad behavior based on supervised machine learning

Mengru Shen, Hongchun Zhang, Te Pi, Xinkai Wang, Nan Xu

Proceedings Volume 12989, Third International Conference on Intelligent Traffic Systems and Smart City (ITSSC 2023); 129890R (2024) https://doi.org/10.1117/12.3023914
Event: Third International Conference on Intelligent Traffic Systems and Smart City (ITSSC 2023), 2023, Xi'an, China

Abstract

The label data on the occurrence of bad behaviors of power company suppliers is seriously unstructured, and a large amount of effective information is difficult to obtain and reuse. This has a certain impact on the subsequent supplier evaluation, especially on the work of the bidding and purchasing department. Therefore, it is necessary to extract the core information in the bad behavior data so that the business department can take targeted management countermeasures in the subsequent bidding and procurement and cooperation process. In this paper, the framework design, model construction and experimental evaluation of the bad behavior label model of power suppliers based on supervised machine learning are carried out according to the characteristics of the bad behavior data of power suppliers.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Mengru Shen, Hongchun Zhang, Te Pi, Xinkai Wang, and Nan Xu "Study on the label generation model of power suppliers’ bad behavior based on supervised machine learning", Proc. SPIE 12989, Third International Conference on Intelligent Traffic Systems and Smart City (ITSSC 2023), 129890R (9 April 2024); https://doi.org/10.1117/12.3023914

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available