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基于MIC特征提取与ICEEMD-RIME-DHKELM的建筑业碳排放预测模型

张新生 聂达文 陈章政

张新生,聂达文,陈章政.基于MIC特征提取与ICEEMD-RIME-DHKELM的建筑业碳排放预测模型[J].环境工程,2025,43(4):46-58. doi: 10.13205/j.hjgc.202504005
引用本文: 张新生,聂达文,陈章政.基于MIC特征提取与ICEEMD-RIME-DHKELM的建筑业碳排放预测模型[J].环境工程,2025,43(4):46-58. doi: 10.13205/j.hjgc.202504005
ZHANG X S,NIE D W,CHEN Z Z.Research on a prediction model for carbon emissions of construction industry based on MIC feature extraction and ICEEMD-RIME-DHKELM[J].Environmental Engineering,2025,43(4):46-58. doi: 10.13205/j.hjgc.202504005
Citation: ZHANG X S,NIE D W,CHEN Z Z.Research on a prediction model for carbon emissions of construction industry based on MIC feature extraction and ICEEMD-RIME-DHKELM[J].Environmental Engineering,2025,43(4):46-58. doi: 10.13205/j.hjgc.202504005

基于MIC特征提取与ICEEMD-RIME-DHKELM的建筑业碳排放预测模型

doi: 10.13205/j.hjgc.202504005
基金项目: 

陕西省教育厅科学研究计划项目“陕西省新型城镇化与低碳发展的耦合协调研究”(23JT018)

详细信息
    作者简介:

    张新生(1978-),男,教授,主要研究方向为城镇化与碳排放、机器学习与应用。zhangxs@xauat.edu.cn

    通讯作者:

    张新生(1978-),男,教授,主要研究方向为城镇化与碳排放、机器学习与应用。zhangxs@xauat.edu.cn

Research on a prediction model for carbon emissions of construction industry based on MIC feature extraction and ICEEMD-RIME-DHKELM

  • 摘要: 为解决建筑业碳排放研究中影响因素选取局限性、数据预处理不足、碳排放复杂动态变化及非线性问题,提出了一种基于最大信息系数(MIC)特征提取、改进互补集合经验模态分解(ICEEMD)、雾凇优化算法(RIME)与深度混合核极限学习机(DHKELM)的建筑业碳排放量预测模型。首先,根据IPCC计算方法,从直接和间接两个方面测算 1992—2021年我国建筑业碳排放量,基于STIRPAT模型选取年末总人口数、国内生产总值、建筑业房屋竣工面积和能源结构等17个影响建筑业碳排放量的因素,然后利用灰色关联分析和MIC方法两阶段筛选出12个关键影响因素;其次,使用ICEEMD将建筑业碳排放量分解为多个平稳序列和一个残差项,并将其分别代入RIME算法优化关键参数后的DHKELM模型中。最后,将各分解序列的预测结果相加获得建筑业碳排放预测值,并对比分析多种基准模型的预测结果。结果显示:MIC-ICEEMD-RIME-DHKELM模型的预测性能最优,其均方根误差、平均绝对误差、平均绝对百分比误差和绝对相关系数分别为0.2782亿t、0.2672亿t、1.3783%和0.9576,均优于其他模型,证明该模型适用于建筑业碳排放量的预测。该研究成果为建筑业的低碳发展提供理论支持和技术参考。
  • 1  软雾凇粒子运动图

    1.  Soft-rime particle motion diagram

    2  硬雾凇粒子穿刺图

    2.  Hard-rime particle puncture diagram

    3  DHKELM模型结构

    3.  DHKELM model structure diagram

    4  基于MIC特征提取与ICEEMD-RIME-DHKELM模型预测流程

    4.  Flowchart of prediction based on MIC feature extraction and ICEEMD-RIME-DHKELM model

    5  1992—2021年中国建筑业碳排放趋势

    5.  China construction industry carbon emissions trends from 1992 to 2021

    6  建筑业碳排放影响因素MIC系数热力图

    6.  Heatmap of MIC coefficients of influencing factors of construction industry carbon emissions

    7  ICEEMD分解后的建筑业碳排放量序列图

    年份

    7.  Decomposed construction industry carbon emissions series diagram after ICEEMD analysis

    8  分解后各imf j 和res预测结果对比

    8.  Comparisons of prediction results for each decomposed imf j and res

    9  各模型预测结果对比

    9.  Comparison chart of prediction results from various models

    10  各模型相对误差对比

    10.  Comparisons of relative errors for each model

    11  各模型平均相对误差雷达图

    11.  Radar chart of average relative errors for each model

    1  能源碳排放系数22

    1.   Energy carbon emission coefficients22

    能源种类碳排放系数能源种类碳排放系数
    原煤0.7143 kg C/kg燃料油1.4286 kg C/kg
    焦炭0.9714 kg C/kg石油沥青1.3307 kg C/kg
    原油1.4286 kg C/kg液化石油气1.7143 kg C/kg
    汽油1.4714 kg C/kg天然气1.33 kg C/kg
    煤油1.4714 kg C/kg热力34.12 kg C/GJ
    柴油1.4571 kg C/kg电力0.345 kg C/(kW·h)
    下载: 导出CSV

    2  建筑材料碳排放系数2324

    2.   Carbon emission coefficients of building materials2324

    建筑材料种类钢材木材水泥平板玻璃铝材
    碳排放系数1.789 kg C/kg-842.8 kg C/m30.815 kg C/kg0.966 kg C/kg2.6 kg C/kg
    下载: 导出CSV

    3  积极贪婪选择机制伪代码28

    3.   Pseudo-code of the hard-rime puncture mechanism

    Initialize population R
    Get the current optimal agent and optimal fitness
    WhiletT
    Coefficient of adherence E = sqrt(t/T)
    Ifr2 < E
    Update rime location by the soft-rime search strategy
    End If
    Ifr3 < Normalize fitness of Si
    Cross updating between agents by the hard-rime puncture mechanism
    End If
    If F(Rinew ) < F(Ri )
    Select the best solution over the inferior one using positive greedy selection
    End If
    t = t + 1
    End while
    下载: 导出CSV

    4  建筑业碳排放影响因素

    4.   Factors influencing construction industry carbon emissions

    结构影响因素序号
    人口年末总人口数x1
    人口密度x2
    城镇化率x3
    财富国内生产总值x4
    第三产业增加值x5
    全社会固定资产投资x6
    居民人均可支配收入x7
    建筑业劳动生产率x8
    建筑业生产总值x9
    技术建筑业房屋竣工面积x10
    单位GDP能源消耗x11
    一次能源生产总量x12
    建筑业企业从业人员x13
    能源结构x14
    能源强度x15
    能源加工转换效率x16
    投入产出率x17
    下载: 导出CSV

    5  灰色关联分析排名及MIC系数

    5.   Grey relational analysis ranking and MIC coefficients

    影响因素灰色关联度排名MIC系数
    建筑业劳动生产率0.92911
    建筑业房屋竣工面积0.92321
    居民人均可支配收入0.91831
    能源强度0.91341
    建筑业生产总值0.90651
    人口密度0.90461
    第三产业增加值0.89971
    建筑业企业从业人员0.8681
    一次能源生产总量0.85491
    城镇化率0.814101
    能源加工转换效率0.79111
    年末总人口数0.77121
    全社会固定资产投资0.77120.92
    国内生产总值0.766140.5
    能源结构0.746150.72
    投入产出率0.736160.53
    单位GDP能源消耗0.682170.53
    下载: 导出CSV

    6  各IMF序列的皮尔逊相关系数

    6.   Pearson correlation coefficient of each IMF series

    IMF序列imf1imf2imf3res
    皮尔逊相关系数0.258640.2936670.3520660.925142
    下载: 导出CSV

    7  模型相关参数及对应数值

    7.   Model-related parameters and corresponding values

    DHKELM模型基础参数对应数值RIME算法优化相关参数对应数值
    输入层神经元数12L2正则化参数864.1517
    输出层神经元数1顶层KELM惩罚参数0.001
    隐含层数量3核函数1的权重0.365
    激活函数Sigmoid隐含层节点数9
    核函数RBF99
    poly17
    下载: 导出CSV

    8  各模型预测性能评价指标对比

    8.   Comparison of prediction performance evaluation indicators for each model

    模型MAE/108 tRMSE/108 tMAPE/%R2
    MIC-ICEEMD-RIME-DHKELM0.26720.27821.37830.9576
    ICEEMD-RIME-DHKELM0.21340.44812.31000.8831
    MIC-ICEEMD-WOA-DHKELM0.38620.41911.98170.9038
    MIC-ICEEMD-SSA-DHKELM0.39030.41222.01670.9070
    MIC-RIME-DHKELM0.34830.36621.78000.9266
    MIC-ICEEMD-DHKELM0.47160.49602.43830.8654
    MIC-ICEEMD-KELM0.53780.55592.78000.8308
    下载: 导出CSV
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  • 收稿日期:  2024-04-29
  • 录用日期:  2024-07-17
  • 修回日期:  2024-07-02
  • 刊出日期:  2025-04-01

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