Prediction of surface temperature of building surrounding envelopes using holistic microclimate ENVI-met model

被引:37
|
作者
Forouzandeh, Aysan [1 ]
机构
[1] Leibniz Univ Hannover, Inst Bldg Phys, Dept Civil Engn & Geodet Sci, Appelstr 9A, D-30167 Hannover, Germany
关键词
ENVI-met; Building surrounding envelopes; Surface temperature; LONGWAVE RADIATION; WIND ENVIRONMENT; SIMULATION; CFD;
D O I
10.1016/j.scs.2021.102878
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Simulating the building's surface condition as one of the important factors helps to predict the building's energy consumption, mould growth and deterioration of materials. A microclimate ENVI-met model is one type of the existing software that can predict the building's surface temperature by considering the urban texture as well as the physical properties of the material. Since accurate determination of climatic variables largely depends on the adjustment of different computational parameters, this paper aims to evaluate the reliability of the ENVI-met with different model settings for both summer and winter in Hanover, Germany. According to the model verification, it is suggested to apply full-forcing weather data based on cloud cover for simulations and set a building's inside temperature constant during the winter and vary during the summer. Also, comparing the different facade modes shows that for the exposed smooth facade, the predicted surface temperature based on DIN 6946 is more accurate than the MO method. Finally, the accuracy of the model in predicting the building's surface temperature varies, depending on the urban structure and climate condition. The model is more reliable in common urban areas than semi-closed spaces. Moreover, an average RMSE during summer is similar to 2.2 degrees C higher than in winter.
引用
收藏
页数:16
相关论文
共 50 条
  • [41] Calibrated surface temperature forecasts from the Canadian ensemble prediction system using Bayesian model averaging
    Hamill, Thomas M.
    MONTHLY WEATHER REVIEW, 2007, 135 (12) : 4226 - 4230
  • [42] Seven-day sea surface temperature prediction using a 3DConv-LSTM model
    Wei, Li
    Guan, Lei
    FRONTIERS IN MARINE SCIENCE, 2022, 9
  • [43] Improvement of ENSO prediction using a linear regression model with a southern Indian Ocean sea surface temperature predictor
    Dominiak, S
    Terray, P
    GEOPHYSICAL RESEARCH LETTERS, 2005, 32 (18) : 1 - 4
  • [44] Calibrated surface temperature forecasts from the Canadian ensemble prediction system using Bayesian model averaging - Reply
    Wilson, Laurence J.
    Beauregard, Stephane
    Raftery, Adrian E.
    Verret, Richard
    MONTHLY WEATHER REVIEW, 2007, 135 (12) : 4231 - 4236
  • [45] Prediction of the diurnal warming of sea surface temperature using an atmosphere-ocean mixed layer coupled model
    Noh, Yign
    Lee, Eunjeong
    Kim, Dong-Hoon
    Hong, Song-You
    Kim, Mee-Ja
    Ou, Mi-Lim
    JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS, 2011, 116
  • [46] A PREDICTION MODEL FOR SNOWMELT, SNOW SURFACE-TEMPERATURE AND FREEZING DEPTH USING A HEAT-BALANCE METHOD
    KONDO, J
    YAMAZAKI, T
    JOURNAL OF APPLIED METEOROLOGY, 1990, 29 (05): : 375 - 384
  • [47] Prediction of mean radiant temperature distribution around a building in hot summer days using optimized multilayer neural network model
    Xie, Yuquan
    Ishida, Yasuyuki
    Hu, Jialong
    Mochida, Akashi
    Sustainable Cities and Society, 2022, 84
  • [48] Prediction of mean radiant temperature distribution around a building in hot summer days using optimized multilayer neural network model
    Xie, Yuquan
    Ishida, Yasuyuki
    Hu, Jialong
    Mochida, Akashi
    SUSTAINABLE CITIES AND SOCIETY, 2022, 84
  • [49] Prediction Model for the Internal Temperature of a Greenhouse with a Water-to-Water Heat Pump Using a Pellet Boiler as a Heat Source Using Building Energy Simulation
    Lee, Chung-Geon
    Cho, La-Hoon
    Kim, Seok-Jun
    Park, Sun-Yong
    Kim, Dae-Hyun
    ENERGIES, 2022, 15 (15)
  • [50] A spatiotemporal deep learning model for sea surface temperature field prediction using time-series satellite data
    Xiao, Changjiang
    Chen, Nengcheng
    Hu, Chuli
    Wang, Ke
    Xu, Zewei
    Cai, Yaping
    Xu, Lei
    Chen, Zeqiang
    Gong, Jianya
    ENVIRONMENTAL MODELLING & SOFTWARE, 2019, 120