对中国二氧化碳排放预测的统计和机器学习模型进行比较研究
Xiangqian Li1, Xiaoxiao Zhang2
1School of Statistics, Capital University of Economics and Business, Beijing, 100070, People's Republic of China.
Environmental science and pollution research international
|October 22, 2023
概括
长短期记忆 (LSTM) 模型在预测中国每日二氧化碳 (CO2) 排放方面表现出色,优于统计和其他机器学习方法. 这种先进的预测有助于制定有效的排放控制策略和可持续的未来.
科学领域:
- 环境科学 环境科学
- 气候变化研究 气候变化研究
- 数据科学数据科学数据科学
背景情况:
- 增加的二氧化碳 (CO2) 排放是全球变暖的主要驱动因素.
- 准确的二氧化碳排放预测对于有效的气候变化缓解战略至关重要.
- 接近实时的每日二氧化碳排放数据对于及时的政策干预至关重要.
研究的目的:
- 确定中国近乎实时的每日二氧化碳排放的最佳预测模型.
- 为了比较统计和机器学习模型对二氧化碳排放预测的性能.
- 使用平均平方误差,根平均平方误差,平均绝对误差,平均绝对百分比误差和确定系数来评估模型.
主要方法:
- 利用从2020年1月1日到2022年9月30日的单变量每日时间序列数据.
- 提出并评估了六种预测模型:灰色预测 (GM(1,1)),ARIMA,SARIMAX,人工神经网络 (ANN),随机森林 (RF) 和长短期记忆 (LSTM).
- 使用五个关键统计指标 (MSE,RMSE,MAE,MAPE,R2) 评估模型性能.
主要成果:
- 机器学习模型 (ANN,RF,LSTM) 的表现始终优于统计模型 (GM(1,1),ARIMA,SARIMAX).
- 在所有评估指标上,LSTM 模型表现出了卓越的性能.
- 在LSTM的测试中,MSE达到3.5179e-04,RMSE达到0.0187,MAE达到0.0140,MAPE达到14.8291%,R2达到0.9844.
结论:
- 在中国,LSTM模型是最适合近乎实时的每日二氧化碳排放预测的.
- 在捕捉复杂的排放模式方面,LSTM的稳定性支持其在排放预测中的应用.
- 来自LSTM的准确CO2预测可以为有效的缓解策略和环境政策提供数据驱动的决策信息.
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