基于不同神经网络模型的碳峰值预测场景:贵州省的一个案例研究
Da Lian1, Shi Qiang Yang2, Wu Yang3
1China Railway Fifth Bureau Group Co., Ltd., Guiyang, China.
PloS one
|June 25, 2024
概括
这项研究使用先进的人工智能模型预测贵州省未来的碳排放. 鱼优化算法-极端学习机器 (WOA-ELM) 模型为碳中和规划提供了卓越的准确性.
科学领域:
- 环境科学 环境科学
- 气候变化建模模型
- 人工智能在可持续发展中的作用
背景情况:
- 全球变暖需要了解区域碳排放,以实现可持续发展.
- 贵州省,一个中国的地地区,严重依赖化石燃料,使其排放成为一个关键的研究领域.
- 准确的碳排放预测对于实现碳中和目标至关重要.
研究的目的:
- 预测2020年至2040年贵州省的碳排放情况.
- 为了比较不同的人工智能模型的排放预测性能.
- 分析影响峰值碳排放的各种场景.
主要方法:
- 使用反向传播 (BP) 神经网络和极端学习机器 (ELM) 模型进行非线性处理.
- 采用了与能源消耗数据的转换和库存编制方法.
- 应用了灰色相关性分析来识别关键影响因素,并开发了一个鱼优化算法-极端学习机器 (WOA-ELM) 模型.
主要成果:
- 贵州省的碳排放呈现"S"增长趋势.
- 与BP和标准ELM模型相比,WOA-ELM模型显示出更高的预测性能.
- 为基线,高速和低碳场景生成了预测,包括峰值排放时间表.
结论:
- WOA-ELM模型对于预测区域碳排放是有效的.
- 了解排放趋势和影响因素对于贵州的碳中和战略至关重要.
- 场景分析为决策提供了有价值的见解,以实现可持续发展的经济发展.
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