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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2023, Vol. 57 Issue (10): 2106-2115    DOI: 10.3785/j.issn.1008-973X.2023.10.019
    
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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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     
Received: 08 May 2022      Published: 18 October 2023
CLC:  TU 111.195  
Fund:  北京市社会科学基金资助项目（20GLB025）
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Wei NA
Jia-le BING
Pin-yan LIU
Cite this article:

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.

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


基于贝叶斯MCMC方法的城市建筑供热能耗基准

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 
Fig.1 Comparison of building energy consumption prediction models on information volume, modeling complexity, population size, computing resources and time cost
Fig.2 Flowchart outlining of typical steps involved in MCMC simulation
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
Tab.1 Sample composition of proposed training dataset
Fig.3 Trace of potential scale reduction factor
Fig.4 Trace plot of 3 Markov chains in iterations
Fig.5 ACF of residuals of model from 3 Markov chains
Fig.6 Comparison between metered EUIH with calibrated EUIH for heating substation samples in validation dataset of model
Fig.7 Posterior distributions of parameters in mixed effects model
Fig.8 Joint posterior distribution for EUIH of residential buildings in Beijing
Fig.9 Comparison of benchmark of EUIH for residential building between city of IEA number countries and Beijing
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