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Sustainable cities and society, 2021-07, Vol.70, p.102878, Article 102878
2021
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Autor(en) / Beteiligte
Titel
Prediction of surface temperature of building surrounding envelopes using holistic microclimate ENVI-met model
Ist Teil von
  • Sustainable cities and society, 2021-07, Vol.70, p.102878, Article 102878
Ort / Verlag
Elsevier Ltd
Erscheinungsjahr
2021
Quelle
Alma/SFX Local Collection
Beschreibungen/Notizen
  • •Validation performed at seven Facade points in the summer and Winter.•ENVI-met can predict the building’s surface temperature with good accuracy.•The accuracy of the ENVI-met model varies depending on the model adjustments.•ENVI-met is more accurate for common urban areas than the semi-closed spaces. 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 ∼ 2.2 °C higher than in winter.
Sprache
Englisch
Identifikatoren
ISSN: 2210-6707
eISSN: 2210-6715
DOI: 10.1016/j.scs.2021.102878
Titel-ID: cdi_crossref_primary_10_1016_j_scs_2021_102878

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