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|Title:||Bim and data-driven predictive analysis of optimum thermal comfort for indoor environment||Authors:||Gan, Vincent J. L.
Kwok, H. L.
|Keywords:||Building information modelling
|Issue Date:||27-Jun-2021||Publisher:||MDPI AG||Citation:||Gan, Vincent J. L., Luo, Han, Tan, Yi, Deng, Min, Kwok, H. L. (2021-06-27). Bim and data-driven predictive analysis of optimum thermal comfort for indoor environment. Sensors 21 (13) : 4401. ScholarBank@NUS Repository. https://doi.org/10.3390/s21134401||Rights:||Attribution 4.0 International||Abstract:||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. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.||Source Title:||Sensors||URI:||https://scholarbank.nus.edu.sg/handle/10635/232443||ISSN:||1424-8220||DOI:||10.3390/s21134401||Rights:||Attribution 4.0 International|
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