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Research on Carbon Emission Trends of Enterprises in a Certain City Based on Electricity-Carbon Coupling and the Prophet Model

Published: 24 October 2024 Publication History

Abstract

As carbon emission monitoring and measurement technologies continue to advance, there is a growing demand for higher accuracy in industrial carbon emission data. This paper introduces a regression analysis-based electrical-carbon coupling measurement algorithm, which establishes a coupled relationship between electricity consumption and carbon emissions based on predicted energy structures within the industry. Carbon emission data is derived through the collection of electrical data, and this algorithm has achieved a prediction accuracy of 99.34% for carbon emissions in the electronics manufacturing sector of a specific city. Furthermore, the paper presents a method for analyzing corporate carbon emission trends based on the electrical-carbon coupling measurement algorithm. This method employs the Prophet model to decompose time-series data into trend, seasonal, and holiday components. The Mean Absolute Percentage Error (MAPE) values for the decomposition functions across various enterprises range from 0.90% to 19.97%, effectively leveraging the value of existing data to provide foundational support for industrial users and power companies in assessing the current and future trends of corporate carbon emissions.

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  1. Research on Carbon Emission Trends of Enterprises in a Certain City Based on Electricity-Carbon Coupling and the Prophet Model

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      cover image ACM Other conferences
      CAIBDA '24: Proceedings of the 2024 4th International Conference on Artificial Intelligence, Big Data and Algorithms
      June 2024
      1206 pages
      ISBN:9798400710247
      DOI:10.1145/3690407
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 24 October 2024

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      Author Tags

      1. Carbon emission prediction
      2. Electricity-carbon coupling
      3. Prophet
      4. Time series decomposition

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