Novel Automatic Feature Engineering for Carbon Emissions Prediction Base on Deep Learning

Z. Lee, Yun Lin, Zhang Yang, Zhong-Yuan Chen, Wei-Guo Fan, Chen-Hsin Lee
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引用次数: 1

Abstract

The primary cause of global climate change is carbon emissions. The world must urgently reduce carbon emissions to avoid the worst effects of climate change. Understanding the most important features of carbon emissions is the first goal in decreasing carbon emissions. One of the critical issues for carbon emissions is research on feature engineering and prediction. Therefore, we propose a novel automatic feature engineering for carbon emissions. In the proposed algorithm, automatic feature engineering is used to select important features. Furthermore, deep learning is used to reduce the prediction error for carbon emissions. The proposed algorithm, decision trees, and linear regression are compared with previous methods using the Kaggle dataset of carbon emissions. The results demonstrate that the proposed algorithm selects the four most important features from the Kaggle dataset of carbon emissions. The proposed algorithm also enhances and lessens the root mean square error (RMSE) of the prediction. The proposed algorithm outperforms the other approaches.
基于深度学习的碳排放预测自动特征新方法
全球气候变化的主要原因是碳排放。世界必须紧急减少碳排放,以避免气候变化的最坏影响。了解碳排放的最重要特征是减少碳排放的首要目标。碳排放特征工程与预测是碳排放研究的关键问题之一。因此,我们提出了一种新的碳排放自动特征工程。在该算法中,使用自动特征工程来选择重要特征。此外,利用深度学习减少了碳排放的预测误差。利用Kaggle碳排放数据集,将本文提出的算法、决策树和线性回归与之前的方法进行了比较。结果表明,该算法能够从Kaggle碳排放数据集中筛选出4个最重要的特征。该算法还增强和减小了预测的均方根误差(RMSE)。该算法优于其他方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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