EN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
Real-time prediction model for carbon emission using BP neural network based on clustering algorithm and Bayesian optimization
- 热力发电 2025年第11期 页码：126-135
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DOI：10.19666/j.rlfd.202502015
中图分类号：-
纸质出版：2025
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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]. 热力发电, 2025,(11):126-135.
姜宇恒, 乔宗良, 李逗, et al. Real-time prediction model for carbon emission using BP neural network based on clustering algorithm and Bayesian optimization[J]. Thermal Power Generation, 2025, (11): 126-135.
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]. 热力发电, 2025,(11):126-135. DOI： 10.19666/j.rlfd.202502015.
姜宇恒, 乔宗良, 李逗, et al. Real-time prediction model for carbon emission using BP neural network based on clustering algorithm and Bayesian optimization[J]. Thermal Power Generation, 2025, (11): 126-135. 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。
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references
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