A Data-Driven Analysis Method for the Trajectory of Power Carbon Emission in the Urban Area.

IF 2.6 4区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Big Data Pub Date : 2025-02-01 Epub Date: 2023-06-16 DOI:10.1089/big.2022.0299

Yi Gao, Dawei Yan, Xiangyu Kong, Ning Liu, Zhiyu Zou, Bixuan Gao, Yang Wang, Yue Chen, Shuai Luo

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引用次数: 0

Abstract

"Industry 4.0" aims to build a highly versatile, individualized digital production model for goods and services. The carbon emission (CE) issue needs to be addressed by changing from centralized control to decentralized and enhanced control. Based on a solid CE monitoring, reporting, and verification system, it is necessary to study future power system CE dynamics simulation technology. In this article, a data-driven approach is proposed to analyzing the trajectory of urban electricity CEs based on empirical mode decomposition, which suggests combining macro-energy thinking and big data thinking by removing the barriers among power systems and related technological, economic, and environmental domains. Based on multisource heterogeneous mass data acquisition, effective secondary data can be extracted through the integration of statistical analysis, causal analysis, and behavior analysis, which can help construct a simulation environment supporting the dynamic interaction among mathematical models, multi-agents, and human participants.

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城市电力碳排放轨迹的数据驱动分析方法。

“工业4.0”旨在为商品和服务建立一个高度通用、个性化的数字化生产模式。碳排放问题需要通过从集中控制转向分散和加强控制来解决。基于可靠的CE监测、报告和验证系统，有必要研究未来电力系统CE动态仿真技术。本文提出了一种基于经验模态分解的数据驱动方法，通过消除电力系统与相关技术、经济和环境领域之间的障碍，将宏观能源思维与大数据思维相结合，来分析城市电力消费成本的轨迹。在多源异构海量数据采集的基础上，通过统计分析、因果分析和行为分析相结合，提取有效的辅助数据，构建支持数学模型、多智能体和人类参与者之间动态交互的仿真环境。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Big Data COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

9.10

自引率

2.20%

发文量

期刊介绍： Big Data is the leading peer-reviewed journal covering the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data. The Journal addresses questions surrounding this powerful and growing field of data science and facilitates the efforts of researchers, business managers, analysts, developers, data scientists, physicists, statisticians, infrastructure developers, academics, and policymakers to improve operations, profitability, and communications within their businesses and institutions. Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address current challenges and enforce effective efforts to organize, store, disseminate, protect, manipulate, and, most importantly, find the most effective strategies to make this incredible amount of information work to benefit society, industry, academia, and government. Big Data coverage includes: Big data industry standards, New technologies being developed specifically for big data, Data acquisition, cleaning, distribution, and best practices, Data protection, privacy, and policy, Business interests from research to product, The changing role of business intelligence, Visualization and design principles of big data infrastructures, Physical interfaces and robotics, Social networking advantages for Facebook, Twitter, Amazon, Google, etc, Opportunities around big data and how companies can harness it to their advantage.