School of Architecture and Art, Central South University, Changsha 410083, China.
College of Landscape Architecture and Art Design, Hunan Agricultural University, Changsha 410128, China.
Int J Environ Res Public Health. 2021 Aug 9;18(16):8411. doi: 10.3390/ijerph18168411.
The window-to-wall ratio (WWR) significantly affects the indoor thermal environment, causing changes in buildings' energy demands. This research couples the "Envi-met" model and the "TRNSYS" model to predict the impact of the window-to-wall ratio on indoor cooling energy demands in south Hunan. With the coupled model, "Envi-met + TRNSYS", fixed meteorological parameters around the exterior walls are replaced by varied data provided by Envi-met. This makes TRNSYS predictions more accurate. Six window-to-wall ratios are considered in this research, and in each scenario, the electricity demand for cooling is predicted using "Envi-met + TRNSYS". Based on the classification of thermal perception in south Hunan, the TRNSYS predictions of the electricity demand start with 30 °C as the threshold of refrigeration. The analytical results reveal that in a 6-storey residential building with 24 households, in order to maintain the air temperature below 30 °C, the electricity required for cooling buildings with 0% WWR, 20% WWR, 40% WWR, 60% WWR, 80% WWR, and 100% WWR are respectively 0 KW·h, 19.6 KW·h, 133.7 KW·h, 273.1 KW·h, 374.5 KW·h, and 461.9 KW·h. This method considers the influence of microclimate on the exterior wall and improves the accuracy of TRNSYS in predicting the energy demand for indoor cooling.
窗墙比(WWR)显著影响室内热环境,导致建筑物能源需求发生变化。本研究将“Envi-met”模型和“TRNSYS”模型相结合,预测窗墙比对湘南地区室内冷却能耗的影响。通过使用“Envi-met+TRNSYS”模型,将外墙周围固定的气象参数替换为 Envi-met 提供的变化数据,从而提高了 TRNSYS 预测的准确性。本研究考虑了六种窗墙比,在每种情况下,都使用“Envi-met+TRNSYS”来预测冷却能耗。基于湘南地区的热感觉分类,TRNSYS 预测的制冷用电需求以 30°C 为阈值。分析结果表明,在一栋 6 层住宅建筑中,有 24 户家庭,为了使空气温度保持在 30°C 以下,具有 0% WWR、20% WWR、40% WWR、60% WWR、80% WWR 和 100% WWR 的建筑物的冷却用电需求分别为 0 KW·h、19.6 KW·h、133.7 KW·h、273.1 KW·h、374.5 KW·h 和 461.9 KW·h。该方法考虑了小气候对外墙的影响,提高了 TRNSYS 预测室内冷却能耗需求的准确性。