基于机器学习的优化回归模型的开发和性能比较,用于预测与能源相关的二氧化碳排放
Ebru Koca Akkaya1, Ali Volkan Akkaya2
1Department of Environmental Engineering, Yildiz Technical University, Esenler, 34220, Istanbul, Türkiye. ekoca@yildiz.edu.tr.
Environmental science and pollution research international
|November 15, 2023
概括
准确预测二氧化碳 (CO2) 排放对于可持续能源规划至关重要. 这项研究开发了一种机器学习模型,优化了高斯过程回归,以高准确度可靠地预测国家二氧化碳排放.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 气候变化建模模型
背景情况:
- 准确的国家二氧化碳 (CO2) 排放预测对于能源规划和实现可持续的低碳目标至关重要.
- 有效的减排策略依赖于精确预测二氧化碳的产量.
研究的目的:
- 开发一个通用的机器学习模型,用于预测68个国家的国家二氧化碳排放.
- 为了比较各种机器学习回归模型的性能,以预测二氧化碳排放.
主要方法:
- 开发了使用支持向量回归,树集和高斯过程回归的九个预测模型.
- 调整了这些机器学习模型的超参数,使用贝叶斯优化来提高预测准确性.
- 使用诸如MSE,RMSE,MAE,MAPE和R平方等指标评估模型性能.
主要成果:
- 优化的高斯过程回归模型表现出卓越的性能,其 R 平方值为 0.9998.
- 最好的模型的关键性能指标包括MSE=106.68,RMSE=10.328,MAE=4.904和MAPE=3.38%.
- 该模型显示了许多国家的强大而准确的二氧化碳排放预测.
结论:
- 优化的高斯过程回归模型是预测国家二氧化碳排放的可靠和高度准确的工具.
- 这种预测能力支持对能源战略和气候变化缓解努力的知情决策.
- 开发的模型提供了一个有希望的方法,通过准确的排放预测来实现可持续和低碳的未来.
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