CO<sub>2</sub> Emission Prediction of Coal-fired Power Plants With Principal Component Analysis and Neural Network

LIN Feng; DING Honglei; WEI Chaojie; HAN Fan; XU Yifeng; WANG Wenhuan; PAN Weiguo

doi:10.13334/j.0258-8013.pcsee.242123

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CO₂ Emission Prediction of Coal-fired Power Plants With Principal Component Analysis and Neural Network

更新时间：2026-10-12

- CO₂ Emission Prediction of Coal-fired Power Plants With Principal Component Analysis and Neural Network
- Vol. 46, Issue 1, Pages: 305-314(2026)
- 作者机构：
  
  上海电力大学能源与机械工程学院，上海市浦东新区,201306
- 作者简介：
- 基金信息：
- DOI：10.13334/j.0258-8013.pcsee.242123
  CLC：
- Received：06 January 2026，
  
  Published：2026
- 稿件说明：
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LIN Feng, DING Honglei, WEI Chaojie, et al. CO₂ Emission Prediction of Coal-fired Power Plants With Principal Component Analysis and Neural Network[J]. 2026, 46(1): 305-314. DOI: 10.13334/j.0258-8013.pcsee.242123.
DOI：

LIN Feng, DING Honglei, WEI Chaojie, et al. CO₂ Emission Prediction of Coal-fired Power Plants With Principal Component Analysis and Neural Network[J]. 2026, 46(1): 305-314. DOI: 10.13334/j.0258-8013.pcsee.242123. DOI：

摘要

燃煤机组作为能源转换的关键设施，当前在电力生产中仍占据重要地位，但是其复杂性显著增加了二氧化碳排放预测的难度。该文提出一种二氧化碳排放预测模型，将主成分分析与反向传播神经网络相结合。首先，通过缺失值与异常值处理等手段对燃煤电厂的碳排放数据进行预处理。然后，通过相关性分析筛选出与碳排放关系密切的变量，并结合主成分分析方法，在保证关键信息不丢失的前提下进一步降低数据维度，构建燃煤电厂二氧化碳排放预测模型。以一台660 MW装机容量的燃煤发电机组为例，依据其运行参数开展仿真验证，模型的预测结果误差小，拟合效果良好。结果表明，该模型能够有效预测二氧化碳排放，可为燃煤电厂制定二氧化碳减排策略提供参考。

Abstract

As a key facility for energy conversion

coal-fired units still occupy an important position in power production. However

their complexity significantly increases the difficulty of predicting carbon dioxide emissions. A carbon dioxide emission prediction model is proposed in the paper. This model combines principal component analysis with backpropagation neural network. Firstly

methods such as missing value processing and outlier processing are used to preprocess the data related to carbon emissions of coal-fired power plants. Then

variables with strong correlation to carbon emissions are screened out through correlation analysis. Combined with the principal component analysis method

the data dimension is further reduced on the premise of ensuring that key information is not lost

and a carbon dioxide emission prediction model for coal-fired power plants is constructed. Taking a coal-fired generator set with an installed capacity of 660 MW as an example

simulation verification is carried out based on its operating parameters. The prediction results show small errors and good fitting effects. This shows that this model can effectively predict carbon dioxide emissions and can provide a reference for carbon dioxide emission reduction in coal-fired power plants.

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CO2 Emission Prediction of Coal-fired Power Plants With Principal Component Analysis and Neural Network

DOI：10.13334/j.0258-8013.pcsee.242123

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Abstract

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Keywords

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CO₂ Emission Prediction of Coal-fired Power Plants With Principal Component Analysis and Neural Network