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浙江大学学报(工学版)
浙江大学学报(工学版)  2023, Vol. 57 Issue (10): 2106-2115    DOI: 10.3785/j.issn.1008-973X.2023.10.019
土木工程     
基于贝叶斯MCMC方法的城市建筑供热能耗基准
那威1(),邴佳乐1,刘品妍2
1. 北京建筑大学 环境与能源工程学院，北京 100044
2. 北京建筑大学 建筑与城市规划学院，北京 100044
Urban building heating energy consumption benchmark based on Bayesian MCMC method
Wei NA1(),Jia-le BING1,Pin-yan LIU2
1. School of Environment and Energy Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
2. School of Architecture and Urban Planning, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
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摘要：

城市尺度建筑能耗基准的研究模型需解决建筑样本数据有限、输入不确定性强、建模复杂度和计算成本高的问题，据此提出贝叶斯框架的混合效应模型. 利用城市气候、建筑分布和建造状况等特征指标作为模型输入，采集223座居住建筑的热力站能耗监测数据作为测试集样本，通过马尔科夫链蒙特卡洛（MCMC）方法进行重要性采样，并预测城市尺度建筑供热能耗强度（EUIH）. 混合效应模型通过48座居住建筑的热力站能耗监测数据进行验证，预测精度指标归一比平均偏差（NMBE）为0.400%，均方根误差（RMSE）为0.034，均方根误差变异系数（CVRMSE）为13.100%. 结果显示模型能够有效地预测和校准建筑供热能耗预测和校准，模型的EUIH点估计和区间估计结果可以作为城市建筑供热能耗基准依据. 北京市城市建筑的供热能耗基准与国际能源署（IEA）国家的相关基准进行对比，反映出北京市居住建筑供热能耗较低及节能潜力较小.
关键词： 建筑能耗基准;  贝叶斯理论;  马尔科夫链蒙特卡洛方法;  城市尺度;  建筑能耗预测    
Abstract:

The model of benchmarking building energy use in city-scale should tackle the challenges on reliable evaluation and calibration for building energy consumption, regarding issues of insufficient building samples, input uncertainties, modelling complexity, and computing cost in the development. A mixed-effect probability model was proposed in accordance with Bayesian theory. The characteristic indicators such as urban climate, building distribution and construction status were used as model inputs. The metering data of energy consumption from 223 heating substation samples were used as training data. Markov Chain Monte Carlo (MCMC) simulation was utilized to conduct importance sampling, and building energy use intensity for space heating (EUIH) in city-scale were calibrated. The mixed-effect probability model was validated by a new dataset with 48 heating substations, which the values of normalized mean bias error (NMBE), root mean square error (RMSE), and cofficient of variation root mean squared error (CVRMSE) indices were 0.400%, 0.034, 13.100%. The results show that the model can be effectively used to calibrate building energy consumption for heating. The EUIH point and interval estimation results of the model can serve as the benchmark for the urban building heating energy consumption. The proposed benchmark of city-scale building heating energy was compared with that of international energy agercy (IEA) member countries , and the reveal resident building in Beijing had lower EUIH and less potential on the energy conservation for heating.
Key words: building energy consumption benchmark    Bayesian theory    MCMC approach    city-scale    building energy consumption prediction
收稿日期: 2022-05-08 出版日期: 2023-10-18
CLC:  TU 111.195  
基金资助: 北京市社会科学基金资助项目（20GLB025）
作者简介: 那威（1979—），男，副教授. 从事建筑能耗分析和数据挖掘研究. orcid.org/0000-0002-3818-0831. E-mail： nawei@bucea.edu.cn
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引用本文:

那威,邴佳乐,刘品妍. 基于贝叶斯MCMC方法的城市建筑供热能耗基准[J]. 浙江大学学报(工学版), 2023, 57(10): 2106-2115.

Wei NA,Jia-le BING,Pin-yan LIU. Urban building heating energy consumption benchmark based on Bayesian MCMC method. Journal of ZheJiang University (Engineering Science), 2023, 57(10): 2106-2115.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2023.10.019    或    https://www.zjujournals.com/eng/CN/Y2023/V57/I10/2106

图 1  建筑能耗预测模型应用所需信息量、建模难度、样本量、计算资源和时间成本对比
图 2  MCMC方法应用的关键步骤流程图
样本EUIH值区间(GJ/m2) S/座 SP/% A/hm2 AP/%
>0.300 11 5.1 41.8 3.9
0.250~0.300 137 61.3 774.5 71.4
0.200~0.250 27 12.0 61.1 5.6
<0.200 48 21.6 207.6 19.1
总计 223 100 1 085 100
样本中居住建筑所采用居住建筑节能设计标准 S/座 SP/% A/hm2 AP/%
未采用节能设计标准（非节能建筑）采用30%节能设计标准 31 13.9 116.0 10.7
（“一步”节能建筑）采用50%节能设计标准 37 16.6 130.0 12.0
（“二步”节能建筑） 100 44.8 495.0 45.6
采用65%节能设计标准及以上（“三步”节能建筑） 55 24.7 344.0 31.7
总计 223 100 1 085 100
表 1  模型训练集样本分布情况
图 3  潜在尺度缩减因子轨迹
图 4  3条马尔可夫链迭代轨迹图
图 5  模型参数估计中3条马尔可夫链残差自相关函数
图 6  测试集热力站能耗EUIH的观测值和预测值对比
图 7  混合效应模型参数后验分布
图 8  北京市居住建筑EUIH的联合后验分布
图 9  国际能源署成员国与北京的居住建筑EUIH基准值对比
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