基于人工智能的电力企业碳足迹管理模型的优化
Liangzheng Wu1,2, Kaiman Li1, Yan Huang1
1Energy Development Research Institute, China Southern Power Grid, Guangzhou, China.
PloS one
|January 3, 2025
概括
人工智能 (AI) 优化了电力公司的碳足迹管理. 机器学习识别了排放因素和减少机会,帮助制定节能和减少排放的战略.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 能源政策 能源政策
背景情况:
- 电力行业面临着越来越大的碳足迹管理和减排压力.
- 现有的碳管理模型可能缺乏识别关键排放驱动因素和机会的复杂性.
- 在制定有效的碳减排计划时,对数据驱动策略的需求至关重要.
研究的目的:
- 为使用人工智能 (AI) 的电力企业优化碳足迹管理模型.
- 通过确定主要影响因素和机会,科学制定减少碳排放的战略.
- 为电力行业建立一个全面的碳审计评估系统.
主要方法:
- 开发一个大数据和基于人工智能的碳足迹计算模型.
- 应用机器学习算法来分析电力企业的碳排放数据.
- 使用驱动器状态响应 (DSR) 模型和重量-TOPSIS进行全面评估.
主要成果:
- 确定影响碳排放的关键因素以及电力企业减少碳排放的机会.
- 使用DSR模型对碳审计绩效的评估,包括中国电力资源公司和大唐国际电力公司的案例研究.
- 中国电力资源公司在可再生能源推广方面表现改善,而大唐国际电力公司则出现波动.
结论:
- 该研究为政府,企业和公众提供有关节能和减排成果和挑战的可靠碳信息.
- 人工智能驱动的碳足迹模型和基于DSR的评估系统为电力部门的碳管理提供了一种新的方法.
- 这项研究为在电力行业制定有效的碳减排战略提供了新的视角和基础.
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