Real-time prediction model for carbon emission using BP neural network based on clustering algorithm and Bayesian optimization

姜宇恒; 乔宗良; 李逗; 任少君; 司风琪

doi:10.19666/j.rlfd.202502015

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Real-time prediction model for carbon emission using BP neural network based on clustering algorithm and Bayesian optimization

Thermal Power Generation Vol. 54, Issue 11, Pages: 126-135(2025)

更新时间：2026-08-11

- Real-time prediction model for carbon emission using BP neural network based on clustering algorithm and Bayesian optimization
- 作者机构：
  
  1. 东南大学能源与环境学院
  2. 江苏方天电力有限公司
- 作者简介：
- 基金信息：
- DOI：10.19666/j.rlfd.202502015
  CLC： TP18;X773
- Published：2025
- 稿件说明：
移动端阅览
[1]姜宇恒,乔宗良,李逗,等.基于聚类算法与贝叶斯优化的BP神经网络实时碳排放量预测模型[J],2025,54(11):126-135.
[1]姜宇恒,乔宗良,李逗,等.基于聚类算法与贝叶斯优化的BP神经网络实时碳排放量预测模型[J],2025,54(11):126-135. DOI： 10.19666/j.rlfd.202502015.

DOI：10.19666/j.rlfd.202502015.

摘要

为了构建燃煤电厂碳排放量预测模型，针对燃煤机组普遍缺少入炉煤实时元素分析的问题，依据某百万千瓦机组2023年入炉煤质信息，以低位发热量、挥发分、硫分作为煤质划分依据，采用K-means++算法进行聚类分析，通过相关性分析筛选碳排放量预测模型的输入参数，基于贝叶斯优化的BP神经网络对聚类后各簇数据分别构建碳排放量预测模型，并对升降负荷等工况进行模型测试。结果显示，经煤质聚类后的模型在预测碳排放量时准确性显著提高，与未聚类的模型相比，平均均方根误差和平均相对误差最优情况降低约53.4%、49.2%，特别是在变负荷工况下，预测结果较准确。该方法不仅能有效预测燃煤电厂的碳排放量，还能在煤质复杂多变的情况下保持较高精度。

Abstract

To construct a prediction model for carbon emission from coal-fired power plants and address the problem of general lack of real-time elemental analysis for coal entering the furnace of coal-fired units

according to the in-furnace coal quality information of a million kilowatt unit in 2023

the low calorific value

volatile matter

and sulfur content were used as the basis for coal quality classification

K-means++ algorithm was used for clustering analysis

and correlation analysis was used to screen the input parameters of the carbon emission prediction model. The BP neural network suffered Bayesian optimization was used to construct carbon emission prediction models for each cluster data after clustering

and the models were tested for working conditions such as load increase and decrease. The results show that

the accuracy of the coal quality clustering model in predicting carbon emissions increases significantly. Compared with the non clustered model

the optimal cases of average root mean square error and average relative error reduce by about 53.4% and 49.2%

respectively. Especially under variable load conditions

the predicted results are more in line with the actual values. This indicates that the proposed method can not only effectively predict the carbon emissions of coal-fired power plants

but also maintain high accuracy in the case of complex and variable coal quality.

关键词

Keywords

references

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