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基于VF-LSTM模型监测关键行业的碳排放

Yang Wang¹, Tianchun Xiang¹, Shuai Luo²

¹China State Grid Tianjin Electric Power Company Tianjin Hebei District, Tianjin, China.

|November 22, 2025

概括

本研究介绍了一种保护隐私的VF-LSTM模型,用于监测工业碳排放,提高城市可持续性目标的准确性和数据安全性.

科学领域:

环境科学环境科学
数据科学数据科学数据科学
工业生态学工业生态学

背景情况:

来自工业活动的温室气体排放威胁到城市可持续发展和碳中和目标.
目前的碳排放监测方法缺乏频率,准确性和隐私安全性.
有效的监测对于碳减排战略的知情决策至关重要.

研究的目的:

提出一种新的隐私保护模型,用于监测主要城市行业的碳排放.
解决现有方法的局限性,包括低频率,精度差,隐私不足.
开发一种有效的工具,以实现城市碳峰值和碳中和.

主要方法:

使用长短期内存 (LSTM) 与垂直联合框架 (VF-LSTM) 的隐私保护的"电气-碳"联系模型的开发.
实施VF框架,确保"可用但不可见"的多源数据隐私保护.
使用LSTM准确捕获特定行业的碳排放模式.

主要成果:

VF-LSTM模型在监测钢铁,石化,化学和非铁行业的碳排放方面表现出高准确性和稳定性.
该模型有效地监测了行业一级的碳排放,同时确保数据隐私.
验证证实了模型在真实世界工业数据中的性能.

结论:

拟议的VF-LSTM模型在行业一级碳排放监测方面取得了重大进展.
它为准确和隐私安全的碳跟踪提供了可行的解决方案,支持城市可持续性.
这种方法有助于政府机构在决策中为有效的碳减排努力提供帮助.

关键词:

人工智能的人工智能是人工智能.数据隐私保护数据隐私保护关键行业碳排放监测关键行业碳排放监测垂直联合学习是指垂直联合学习.

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