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Research on Carbon-Electricity Feature Extraction Technology for Enterprises based on Blockchain and Deep Learning Algorithms

Published: 31 July 2024 Publication History

Abstract

Accurate collection and data analysis of enterprise carbon emissions are fundamental for precise estimation of carbon emissions. To address the challenges related to the security of collecting diverse types of enterprise carbon emission data and the effectiveness of extracting heterogeneous data features, this paper proposes a method for extracting carbon-electricity feature information for enterprises based on blockchain and deep learning algorithms. Firstly, by leveraging the decentralized, open, autonomous, and tamper-proof properties of blockchain technology, a comprehensive architecture for collecting diverse types of enterprise carbon emission data is constructed. The blockchain network is utilized to establish an information exchange and data storage center for multiple energy sources such as coal, electricity, gas, heat, and oil. Secondly, a multi-modal fusion neural network model is developed to extract the spatiotemporal distribution features of electricity consumption and carbon emissions within the same enterprise. This model is designed to handle heterogeneous data and capture multidimensional characteristics. Finally, simulation analysis demonstrates that blockchain technology facilitates the exchange of enterprise energy information and enables the extraction of numerical features, categorical features, temporal features, and spatial features related to carbon-electricity. This validation confirms the universality of the proposed model.

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  1. Research on Carbon-Electricity Feature Extraction Technology for Enterprises based on Blockchain and Deep Learning Algorithms

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    PEAI '24: Proceedings of the 2024 International Conference on Power Electronics and Artificial Intelligence
    January 2024
    969 pages
    ISBN:9798400716638
    DOI:10.1145/3674225
    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

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    Published: 31 July 2024

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