Integrating building energy simulation with a machine learning algorithm for evaluating indoor living walls’ impacts on cooling energy use in commercial buildings
Integrating building energy simulation with a machine learning algorithm for evaluating indoor living walls’ impacts on cooling energy use in commercial buildings
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DOI：
10.1016/j.enbuild.2022.112322
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发表时间：
2022-07
影响因子：
6.7
通讯作者：
Liping Wang;M. Witte
中科院分区：
文献类型：
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作者：
Liping Wang;M. Witte
气候变化带来的环境挑战增加了建筑物的能源使用和峰值需求。大多数现存的绿化系统的研究集中在外部应用，如绿色立面和屋顶，间接影响室内环境。很少有研究集中在量化室内绿化系统对建筑能耗的影响。室内绿化系统（如活墙）的冷却效果主要由蒸散（ET）过程来解释，在蒸散过程中，水通过植物的蒸发和蒸腾转移到周围环境。目前的建筑能耗模拟软件如EnergyPlus没有室内绿化系统的建模模块，本研究基于实验数据，采用机器学习算法-高斯混合回归（GMR）建立了ET模型。通过Python的插件功能将一个量化ET过程中的感生和潜生负荷的室内活墙模型与能量模拟软件EnergyPlus集成。美国能源部（DOE）的中型办公楼参考模型进行了修改，并在这项研究中使用，以评估室内活墙的冷却能源使用的影响。对叶面积比、朝向、距窗距离和气候等参数进行了研究，以评估各因素对室内活墙性能的影响。在三个ASHRAE气候条件下，对室内活动墙的冷却效果进行了评估。可观察到的冷却能量节省获得了南部，东部和西部周边区，而北部周边区的节省是可以忽略不计的，在所有三个气候。考虑到来自用于湿度控制的直接膨胀（DX）制冷设备的额外电力使用，当南部周边区域的LFAR = 1.5时，加利福尼亚州洛杉矶的最大冷却电力节省为25.1%，当东部周边区域的LFAR = 0.5时，为14.4%，当北部周边区域的LFAR = 0.3时，为0.3%，当设计日的LFAR = 0.5时，西周边区的平均值为14.5%。
The environmental challenges of climate change increase the energy usage and peak demands of buildings. Most extant studies of greenery systems focus on exterior applications such as green façades and roofs, which indirectly affect the indoor environment. Few studies have focused on quantifying the influence of indoor greenery systems on building energy consumption. The cooling effects of indoor greenery systems such as living walls are largely accounted for by the evapotranspiration (ET) process, in which water is transferred to the ambient environment through the evaporation from and transpiration of plants. Current building energy simulation software such as EnergyPlus does not have a module for modeling indoor greenery systems.In this study, an ET model was created using a machine learning algorithm—Gaussian Mixture Regression (GMR) based on experimental data. An indoor living wall model quantifying sensible and latent loads from the ET process was integrated with the energy simulation software—EnergyPlus through the Python plugin feature. The U.S. Department of Energy (DOE) medium-sized office building reference model was modified and used in this study to evaluate indoor living walls’ impacts on cooling energy use. A parametric study on leaf to floor area ratios (LFAR), orientations, distances from windows, and climates was conducted to evaluate the influence of each factor on indoor living walls’ performance. Cooling effects of indoor living walls were evaluated in three ASHRAE climates with high cooling demands. Observable cooling energy savings were obtained for the south, east, and west perimeter zones while savings for the north perimeter zone was negligible in all three climates. With the consideration of extra electricity use from direct expansion (DX) dehumidification devices for humidity control, the maximum cooling electricity savings in Los Angeles, CA are 25.1 % when LFAR = 1.5 for the south perimeter zone, 14.4 % when LFAR = 0.5 for the east perimeter zone, 0.3 % when LFAR = 0.3 for the north perimeter zone, and 14.5 % when LFAR = 0.5 for the west perimeter zone on the design day.