Core Chinese Journal
Source Journal of CSCD
Source Journal for Chinese Scientific and Technical Papers
Core Journal of RCCSE
Included in JST China
Included in the Hierarchical Directory of High-quality Technical Journals in Architecture Science Field
Volume 53 Issue 7
Jul.  2023
Turn off MathJax
Article Contents
FANG Tao, LIU Ruijie, WANG Yanzheng, ZOU Ran. Comprehensive Optimization and Empirical Verification of Office Building Design Parameters in Cold Regions Based on Nearly Zero Energy Consumption Targets[J]. INDUSTRIAL CONSTRUCTION, 2023, 53(7): 16-24,78. doi: 10.13204/j.gyjzG22120509
Citation: FANG Tao, LIU Ruijie, WANG Yanzheng, ZOU Ran. Comprehensive Optimization and Empirical Verification of Office Building Design Parameters in Cold Regions Based on Nearly Zero Energy Consumption Targets[J]. INDUSTRIAL CONSTRUCTION, 2023, 53(7): 16-24,78. doi: 10.13204/j.gyjzG22120509

Comprehensive Optimization and Empirical Verification of Office Building Design Parameters in Cold Regions Based on Nearly Zero Energy Consumption Targets

doi: 10.13204/j.gyjzG22120509
  • Received Date: 2022-12-05
  • At present, the nearly zero energy consumption building is showing the trend of scale promotion in China, and the design and evaluation standards from the national to the local have been gradually improving, and most of the standards limit the value of energy-saving design parameters to achieve the goal of near-zero energy consumption. However, office buildings have diverse demands for energy consumption and complex structure of energy consumption, so it is necessary to comprehensively optimize the energy-saving design parameters to achieve the design goal of energy consumption minimization. Based on this, the paper firstly constructed the base model of energy consumption composition and building information for the office buildings in cold regions through investigation, and constructed a parameterized integrated platform with model construction, energy consumption simulation and automatic optimization through grasshopper; secondly, a comparative study of office buildings with different thermal design parameters of building envelopes was conducted, and the importance of lighting energy consumption and the influence law of total energy consumption by multi-parameter interaction with this platform was determined; then, on this basis, the paper optimized the energy consumption-oriented design parameters by genetic algorithm, and the optimal results were obtained; thirdly, with the Comprehensive Lab Building of Shandong Jianzhu University as an example, the paper verified the reliability and accuracy of the research results by comparing the measured data with the simulation data; finally, a correlation analysis of design parameters and energy consumption results was conducted by data statistics software, and a machine learning model was established by training data sets in MATLAB, thus verifying the accuracy of the model.
  • loading
  • [1]
    中国建筑节能协会.2019中国建筑能耗研究报告[J].建筑,2020(7):30-39.
    [2]
    China Association of Building Energy Effectiency. China building energy consumption research report 2020[EB/OL].2022-01-20[2022-12-05]. https://www.cabee.org/site/content/23568.html, 2020.
    [3]
    MACHAIRAS V, TSANGRASSOULIS A, AXARLI K. Algorithms for optimization of building design:a review[J]. Renewable and Sustainable Energy Reviews,2014,31:101-112.
    [4]
    KHEIRI F. A review on optimization methods applied in energy-efficient building geometry and envelope design[J]. Renewable and Sustainable Energy Reviews, 2018, 92(9):897-920.
    [5]
    SAHU M, BHATTACHARJEE B, KAUSHIK S C.Thermal design of air-conditioned building for tropical climate using admittance method and genetic algorithm[J]. Energy and Buildings, 2012, 53(1):1-6.
    [6]
    刘可,徐小东,王伟.以节能为导向的住区形态布局及自动寻优方法研究[J].工业建筑,2021,51(8):1-10

    ,27.
    [7]
    FAN C, CHEN M, TANG R, et al. A novel deep generative modeling-based data augmentation strategy for improving short-term building energy predictions[C]//Building Simulation. Tsinghua University Press, 2022, 15:197-211.
    [8]
    FENG G, DOU B, XU X, et al. Research on energy efficiency design key parameters of envelope for nearly zero energy buildings in cold area[J]. Procedia Engineering, 2017,205:686-693.
    [9]
    LI H, WANG S,CHEUNG H. Sensitivity analysis of design parameters and optimal design for zero/low energy buildings in subtropical regions[J]. Applied Energy, 2018,228:1280-1291.
    [10]
    WANG Y, WEI C. Design optimization of office building envelope based on quantum genetic algorithm for energy conservation[J/OL]. Journal of Building Engineering, 2020,35[2022-12-05]. https://doi.org/10.1016/j.jobe.2020.102048.
    [11]
    YAN H, YAN K, JI G. Optimization and prediction in the early design stage of office buildings using genetic and XGBoost algorithms[J/OL]. Building and Environment, 2022(6):218[2022-12-05].https://doi.org/10.1016/j.buildenv.2022.109081.
    [12]
    KURNITSKI J, PIKAS E, THALFELDT M. Cost optimal and nearly zero energy building solutions for office buildings[J]. Energy and Buildings, 2014, 74:30-42.
    [13]
    LU F, YU Z, ZOU Y, et al. Cooling system energy flexibility of a nearly zero-energy office building using building thermal mass:Potential evaluation and parametric analysis[J/OL]. Energy and Buildings, 2021, 236(3).[2022-12-05]. https://doi.org/10.1016/j.enbuild.2021.110763
    [14]
    XU X, FENG G, CHI D, et al. Optimization of Performance Parameter Design and Energy Use Prediction for Nearly Zero Energy Buildings[J/OL]. Energies, 2018, 11(12)[2022-12-05]. https://doi.org/10.3390/en11123252.
    [15]
    Wallacei. An analytic engine for evolutionary algorithms[EB/OL].[2022-12-05]. https://www.wallacei.com.
    [16]
    Design Builder Software Ltd.DesignBuilder v7.0.2.006[EB/OL].[2022-12-05].https://designbuilder.co.uk.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (82) PDF downloads(4) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return