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[1]刘丹,黎兰豪崎,孙秋悦,等.基于CLSG模型的钢铁行业长期电力负荷预测*[J].中国安全生产科学技术,2026,22(2):80-86.[doi:10.11731/j.issn.1673-193x.2026.02.010]
　LIU Dan,LI Lanhaoqi,SUN Qiuyue,et al.Long-term power load forecasting for the steel industry based on the CLSG model[J].Journal of Safety Science and Technology,2026,22(2):80-86.[doi:10.11731/j.issn.1673-193x.2026.02.010]
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基于CLSG模型的钢铁行业长期电力负荷预测*

《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
22
期数:
2026年2期
页码:
80-86
栏目:
安全工程技术
出版日期:
2026-02-28

文章信息/Info

Title:
Long-term power load forecasting for the steel industry based on the CLSG model
文章编号:
1673-193X(2026)-02-0080-07
作者:
刘丹; 黎兰豪崎; 孙秋悦; 李诗轩; 黄达
（1.武汉理工大学 中国应急管理研究中心，湖北 武汉 430070；
2.武汉理工大学 安全科学与应急管理学院，湖北 武汉 430070）
Author(s):
LIU Dan;  LI Lanhaoqi;  SUN Qiuyue;  LI Shixuan;  HUANG Da
（1.China Research Center for Emergency Management,Wuhan University of Technology,Wuhan Hubei 430070,China;
2.School of Safety Science and Emergency Management,Wuhan University of Technology,Wuhan Hubei 430070,China)
关键词:
电力负荷预测; 完全集成经验模态分解（CEEMDAN）; 长短期记忆网络（LSTM）; 序列到序列（Seq2Seq）; 网格搜索
Keywords:
power load forecasting;  complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN);  long short-term memory (LSTM);  sequence-to-sequence (Seq2Seq);  grid search
分类号:
X934
DOI:
10.11731/j.issn.1673-193x.2026.02.010
文献标志码:
A
摘要:
为克服钢铁行业长期电力高噪声和高负荷带来的非线性和非平稳的预测挑战，提出完全集成经验模态分解（CEEMDAN）、长短期记忆网络（LSTM）与序列到序列结构（Seq2Seq）的CLSG组合预测模型。首先，基于CEEMDAN分解原始负荷序列，提取多尺度模态分量；其次，采用LSTM-Seq2Seq模型捕捉负荷数据的时序依赖关系与序列演化特征，通过网格搜索进行关键参数寻优；最后，以云南曲靖钢铁行业电力负荷数据开展实验验证分析和对比分析。研究结果表明：CLSG模型的平均绝对误差在0.1以内，均方根误差在0.15以内，平均绝对百分比误差在0.2以内，相较于TBA、CRSG、CGSG、MCLS模型，CLSG模型的误差指标值均最小，具有更高的精度与稳定性。研究结果可为钢铁行业电力负荷精准预测与高效管理提供新方法。
Abstract:
In order to overcome the challenges of nonlinear and non-stationary prediction caused by the high noise and high load in the steel industry’s long-term electricity usage,a fully integrated prediction model named CLSG is proposed,which combines Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN),Long Short-Term Memory Network (LSTM),and Sequence-to-Sequence (Seq2Seq) architecture.Firstly,the original load sequence is decomposed based on CEEMDAN,extracting multi-scale modal components.Secondly,the LSTM-Seq2Seq model is employed to capture the temporal dependencies and sequence evolution characteristics of the load data,with key parameters optimized using grid search.Finally,experimental verification and comparative analysis are conducted based on the power load data from the steel industry in Qujing,Yunnan.The results indicate that the CLSG model achieves an average absolute error of less than 0.1,a root mean square error of less than 0.15,and an average absolute percentage error of less than 0.2.Compared to the TBA,CRSG,CGSG,and MCLS models,the CLSG model has the smallest error metrics,demonstrating higher accuracy and stability.The findings provide a new approach for precise prediction and efficient management of electricity loads in the steel industry.

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备注/Memo

备注/Memo:
收稿日期： 2025-11-09
* 基金项目： 国家社会科学基金项目(23BGL280)
作者简介： 刘丹，博士，教授，主要研究方向为公共安全与应急管理、机器学习与智能计算、风险评估与应急决策。
通信作者： 孙秋悦，博士研究生，主要研究方向为公共安全与应急管理。
更新日期/Last Update: 2026-03-09