Back

Power-Carbon Information Management System Based on Machine Learning.

  • Published In: International Journal of High Speed Electronics & Systems, 2025, v. 34, n. 2. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Wang, Ruohan; Chen, Yunlong; Li, Entang; Xing, Hongwei; Zhang, Jianhui; Li, Jing 3 of 3

Abstract

With the deepening reform of the power market and carbon market, great progress has been made in informatization. Power information may be stored in many scattered places, and it is difficult to share data between different departments or systems. This leads to fragmentation and redundancy of information and makes information exchange difficult. Blockchain can improve the reliability of Power-Carbon Management System (briefly described as PCMS for convenience) data processing. PCMS informatization has become the basis for improving the quality and efficiency of project management and maximizing the environmental and economic benefits of the project. Because the power information management system can effectively control the flow of information and resource allocation. Due to the requirement of low-carbon and stable power production, PCMS attaches great importance to the application and implementation of information in power management, but does not attach enough importance to the informatization of power production management. Therefore, this paper analyzed the current situation, characteristics and existing problems of PCMS through machine learning algorithm, then constructed the design principles, and finally proposed the optimization path of PCMS according to the principles. The information collection ability and system control ability of the optimized PCMS were better than the original PCMS. The information collection ability was 14.2% higher than the original, and the system control ability was 9.8% higher than the original. In general, both blockchain and machine learning can improve the data reliability of PCMS. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of High Speed Electronics & Systems. 2025/06, Vol. 34, Issue 2, p1
  • Document Type:Article
  • Subject Area:Information Technology
  • Publication Date:2025
  • ISSN:0129-1564
  • DOI:10.1142/S0129156424400214
  • Accession Number:184999728
  • Copyright Statement:Copyright of International Journal of High Speed Electronics & Systems is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.