This study preprocessed 2000-2019 energy consumption data for 46 key Sichuan
industries using matrix normalization. DBSCAN clustering identified 16 feature
classes to objectively group industries. Penalized regression models were then
applied for their advantages in overfitting control, high-dimensional data
processing, and feature selection - well-suited for the complex energy data.
Results showed the second cluster around coal had highest emissions due to
production needs. Emissions from gasoline-focused and coke-focused clusters
were also significant. Based on this, emission reduction suggestions included
clean coal technologies, transportation management, coal-electricity
replacement in steel, and industry standardization. The research introduced
unsupervised learning to objectively select factors and aimed to explore new
emission reduction avenues. In summary, the study identified industry
groupings, assessed emissions drivers, and proposed scientific reduction
strategies to better inform decision-making using algorithms like DBSCAN and
penalized regression models.

通过矩阵归一化，对四川 46 个关键行业 2000-2019 年的能源消耗数据进行预处理，使用 DBSCAN 聚类识别了 16 个特征类别，应用惩罚回归模型进行了能源数据复杂性的过拟合控制、高维数据处理和特征选择，并找出了二氧化碳排放量最高的第二类煤炭产业集群，也发现了与汽油和焦炭相关的排放显著。基于这一研究结果，提出了减排建议包括清洁煤技术、交通管理、钢铁中的煤电替代和行业标准化。总结来说，该研究通过 DBSCAN 和惩罚回归模型等算法确定了行业集群，评估了排放驱动因素，并提出了科学减排策略，以更好地为决策提供支持。

基于机器学习的碳排放预测与清洁产业转型：四川省案例研究

Carbon Emission Prediction and Clean Industry Transformation Based on  Machine Learning: A Case Study of Sichuan Province

Predicting emissions for gas turbines is critical for monitoring harmful
pollutants being released into the atmosphere. In this study, we evaluate the
performance of machine learning models for predicting emissions for gas
turbines. We compare an existing predictive emissions model, a first
principles-based Chemical Kinetics model, against two machine learning models
we developed based on SAINT and XGBoost, to demonstrate improved predictive
performance of nitrogen oxides (NOx) and carbon monoxide (CO) using machine
learning techniques. Our analysis utilises a Siemens Energy gas turbine test
bed tabular dataset to train and validate the machine learning models.
Additionally, we explore the trade-off between incorporating more features to
enhance the model complexity, and the resulting presence of increased missing
values in the dataset.

本文评估了采用机器学习模型预测燃气轮机排放的性能，在 Siemens Energy 的测试数据集上比较了现有预测排放模型，基于 SAINT 和 XGBoost 开发的两个机器学习模型，以展示采用机器学习技术可获得氮氧化物（NOx）和一氧化碳（CO）的改进预测性能，并探索了包含更多特征以增强模型复杂性与数据集中缺失值增加之间的平衡。

用于预测燃气轮机排放的表格机器学习方法

Tabular Machine Learning Methods for Predicting Gas Turbine Emissions

Machine Learning (ML) workloads have rapidly grown in importance, but raised
concerns about their carbon footprint. Four best practices can reduce ML
training energy by up to 100x and CO2 emissions up to 1000x. By following best
practices, overall ML energy use (across research, development, and production)
held steady at <15% of Google's total energy use for the past three years. If
the whole ML field were to adopt best practices, total carbon emissions from
training would reduce. Hence, we recommend that ML papers include emissions
explicitly to foster competition on more than just model quality. Estimates of
emissions in papers that omitted them have been off 100x-100,000x, so
publishing emissions has the added benefit of ensuring accurate accounting.
Given the importance of climate change, we must get the numbers right to make
certain that we work on its biggest challenges.

通过采用四种最佳实践方法能将机器学习的培训能源降低 100 倍及 CO2 排放量降低 1000 倍。如果整个机器学习领域采用最佳实践，将会减少训练的碳排放量。因此，我们建议机器学习论文中应当明确列出碳排放数据以促进关于模型质量以外的更广泛的竞争。