Gan Vincent J L, Luo Han, Tan Yi, Deng Min, Kwok H L
School of Design and Environment, National University of Singapore, Singapore 117566, Singapore.
Department of Civil and Environmental Engineering, Hong Kong University of Science and Technology, Hong Kong, China.
Sensors (Basel). 2021 Jun 27;21(13):4401. doi: 10.3390/s21134401.
Mechanical ventilation comprises a significant proportion of the total energy consumed in buildings. Sufficient natural ventilation in buildings is critical in reducing the energy consumption of mechanical ventilation while maintaining a comfortable indoor environment for occupants. In this paper, a new computerized framework based on building information modelling (BIM) and machine learning data-driven models is presented to analyze the optimum thermal comfort for indoor environments with the effect of natural ventilation. BIM provides geometrical and semantic information of the built environment, which are leveraged for setting the computational domain and boundary conditions of computational fluid dynamics (CFD) simulation. CFD modelling is conducted to obtain the flow field and temperature distribution, the results of which determine the thermal comfort index in a ventilated environment. BIM-CFD provides spatial data, boundary conditions, indoor environmental parameters, and the thermal comfort index for machine learning to construct robust data-driven models to empower the predictive analysis. In the neural network, the adjacency matrix in the field of graph theory is used to represent the spatial features (such as zone adjacency and connectivity) and incorporate the potential impact of interzonal airflow in thermal comfort analysis. The results of a case study indicate that utilizing natural ventilation can save cooling power consumption, but it may not be sufficient to fulfil all the thermal comfort criteria. The performance of natural ventilation at different seasons should be considered to identify the period when both air conditioning energy use and indoor thermal comfort are achieved. With the proposed new framework, thermal comfort prediction can be examined more efficiently to study different design options, operating scenarios, and changeover strategies between various ventilation modes, such as better spatial HVAC system designs, specific room-based real-time HVAC control, and other potential applications to maximize indoor thermal comfort.
机械通风在建筑物总能耗中占相当大的比例。建筑物中充足的自然通风对于降低机械通风的能耗，同时为居住者维持舒适的室内环境至关重要。本文提出了一种基于建筑信息模型（BIM）和机器学习数据驱动模型的新型计算机框架，以分析自然通风影响下室内环境的最佳热舒适度。BIM提供建筑环境的几何和语义信息，这些信息被用于设置计算流体动力学（CFD）模拟的计算域和边界条件。进行CFD建模以获得流场和温度分布，其结果决定通风环境中的热舒适度指标。BIM-CFD为机器学习提供空间数据、边界条件、室内环境参数和热舒适度指标，以构建强大的数据驱动模型，实现预测分析。在神经网络中，图论领域的邻接矩阵用于表示空间特征（如区域邻接性和连通性），并将区域间气流对热舒适度分析的潜在影响纳入其中。案例研究结果表明，利用自然通风可以节省制冷能耗，但可能不足以满足所有热舒适度标准。应考虑不同季节自然通风的性能，以确定实现空调能源使用和室内热舒适度的时间段。通过所提出的新框架，可以更有效地检查热舒适度预测，以研究不同的设计方案、运行场景以及各种通风模式之间的转换策略，例如更好的空间暖通空调系统设计、基于特定房间的实时暖通空调控制以及其他潜在应用，以最大化室内热舒适度。
