预测重明的二氧化碳排放:一种新的混合预测模型,将灰色相关性分析和深度学习方法结合起来
Yaqi Wang1, Xiaomeng Zhao1, Wenbo Zhu1
1College of Electronic and Information Engineering, Tongji University, Shanghai, 201804, China.
Environmental monitoring and assessment
|September 17, 2024
概括
这项研究提出了一个新的模型,用于预测上海崇明的二氧化碳 (CO2) 排放,帮助实现碳中和目标. 混合方法准确预测排放量,支持当地碳减排政策.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 气候变化研究 气候变化研究
背景情况:
- 准确预测区域二氧化碳 (CO2) 排放对于实现全球碳中和至关重要.
- 上海崇明面临着独特的环境,经济和能源消耗因素影响其二氧化碳排放.
- 现有的二氧化碳计量方法可能受到数据稀缺和不准确的影响.
研究的目的:
- 开发和验证一种新的混合模型,用于预测上海重明的二氧化碳排放.
- 分析影响该地区二氧化碳排放的因素.
- 为当地碳减排政策和可持续发展提供技术支持.
主要方法:
- 用灰色关系分析来确定经济活动,自然条件和能源消耗对二氧化碳排放的影响.
- 采用具有特征堆叠的双通道聚合卷积神经网络 (DCNN) 来捕获空间数据特征.
- 使用一个门式反复单位 (GRU) 网络来分析所识别的特征的时间动态.
主要成果:
- 混合模型在使用会计数据预测二氧化碳排放时表现出高精度,低误差和良好的稳定性.
- 崇明的碳排放量从2000年到2022年呈现上升趋势,与现有研究一致.
- 提出的方法有效地解决了与有限的会计数据和传统计算不准确性相关的挑战.
结论:
- 开发的混合模型为区域二氧化碳排放预测提供了精确而稳定的方法.
- 这些发现为重明的碳排放趋势和促成因素提供了有价值的见解.
- 这项研究为实施有针对性的碳减排战略和促进该地区可持续发展提供了有效的技术支持.
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