Power-Carbon Information Management System Based on Machine Learning

Ruohan Wang, Yunlong Chen, Entang Li, Hongwei Xing, Jianhui Zhang, Jing Li
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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.
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基于机器学习的电力-碳信息管理系统
随着电力市场和碳市场改革的不断深入,信息化建设取得了长足进步。电力信息可能存储在许多分散的地方,不同部门或系统之间难以共享数据。这导致信息的碎片化和冗余,给信息交流带来困难。区块链可以提高电力碳管理系统(为方便起见,简称 PCMS)数据处理的可靠性。PCMS 信息化已成为提高项目管理质量和效率、实现项目环境效益和经济效益最大化的基础。因为电力信息管理系统可以有效控制信息流和资源配置。由于电力生产低碳、稳定的要求,PCMS 非常重视信息化在电力管理中的应用和实施,但对电力生产管理的信息化重视不够。因此,本文通过机器学习算法分析了 PCMS 的现状、特点和存在的问题,进而构建了设计原则,最后根据原则提出了 PCMS 的优化路径。优化后的 PCMS 的信息采集能力和系统控制能力均优于原 PCMS。信息收集能力比原来提高了 14.2%,系统控制能力比原来提高了 9.8%。总的来说,区块链和机器学习都能提高 PCMS 的数据可靠性。
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来源期刊
International Journal of High Speed Electronics and Systems
International Journal of High Speed Electronics and Systems Engineering-Electrical and Electronic Engineering
CiteScore
0.60
自引率
0.00%
发文量
22
期刊介绍: Launched in 1990, the International Journal of High Speed Electronics and Systems (IJHSES) has served graduate students and those in R&D, managerial and marketing positions by giving state-of-the-art data, and the latest research trends. Its main charter is to promote engineering education by advancing interdisciplinary science between electronics and systems and to explore high speed technology in photonics and electronics. IJHSES, a quarterly journal, continues to feature a broad coverage of topics relating to high speed or high performance devices, circuits and systems.
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