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碳排放预测模型:一项审查

Yukai Jin¹, Ayyoob Sharifi², Zhisheng Li³

¹Urban Environmental Science Lab (URBES), Graduate School of Innovation and Practice for Smart Society, Hiroshima University, Higashi-Hiroshima, 739-8529, Japan; School of Civil and Transportation Engineering, Guangdong University of Technology, Guangdong, 510006, China.

The Science of the total environment

|April 10, 2024

概括

有效的碳排放预测模型 (CEPM) 对于减缓气候变化至关重要. 本次审查突出了统计和神经网络模型,强调了优化技术和对二氧化碳排放趋势的优化后准确度的提高.

科学领域:

环境科学环境科学
气候建模气候模型
数据科学数据科学数据科学

背景情况:

对温室效应的日益担忧需要强大的碳排放预测模型 (CEPM).
了解和预测二氧化碳排放趋势对于有效的气候变化减缓战略至关重要.

研究的目的:

根据其主要功能:预测,优化和因素选择,审查和分类现有的CEPM.
分析不同建模方法的流行和演变,特别是统计和神经网络模型.
评估优化技术对CEPM准确性的影响,并确定影响碳排放的关键因素.

主要方法:

对147项CEPM研究进行了全面的文献综述.
基于功能 (预测,优化,因素选择) 和方法 (统计,神经网络,元启发学) 的模型分类.
对模型性能指标的分析,专注于优化前后的根平均平方误差 (RMSE).

主要成果:

统计模型是最普遍的 (75%),其次是神经网络模型 (21.8%),神经网络使用在2019-2022年间显著增加.
超启发模型被广泛用于优化 (94.4%),主要侧重于参数和结构优化.
优化显著提高了CEPM的准确性,大幅降低了RMSE值.

结论:

CEPM是缓解气候变化的重要工具,其建模技术正在不断进步.
优化策略,特别是元启发式方法,在提高预测准确性方面发挥着关键作用.
进一步的研究应考虑已识别的因素,评估方法和时空尺度,以便进行更全面的碳排放分析.

关键词:

人工智能的人工智能是人工智能.碳排放排放碳排放排放排放气候变化缓解减缓气候变化缓解减缓机器学习是机器学习.神经网络的神经网络的神经网络预测模型的预测模型.

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