Incorporating online monitoring data into fast prediction models towards the development of artificial intelligent ventilation systems

Abstract It has proved that LLVM (Low-dimensional Linear Ventilation Models)-based ANN (artificial neural network) method is able to realize ventilation online control (modes or airflow rates) based on indoor pollutant response. However, it is challenging to rapidly predict indoor pollutant concentration due to the difficulty of identifying pollutant sources (location and strength). Therefore, we will incorporate monitoring techniques to modelling that is able to efficiently predict pollutant concentration, aiming for ventilation online control. A large database was firstly constructed using experiment-validated CFD (Computational Fluid Dynamics) simulations considering different ACHs (air change rates per hour) and individual pollutant sources. Next, LLVM method was utilized to process CFD data, further yielding low-dimensional database for ANN predictions. We then carried out a series of ANN predictions input with monitored concentration from different sensor layouts (i.e., positions and numbers). It is found that well-deployed sensors would provide satisfying inputs for ANN predictions. Suggestions were also given for the sensors placement that should be located in the well-mix zone (e.g. outlet region), but avoiding along the same or parallel with the main flow stream region and near the inlet zone. These findings will further provide strategies of sensor deployment and move crucial steps forward for ventilation intelligent control.

[1]  Zhuangbo Feng,et al.  Study on the impacts of human walking on indoor particles dispersion using momentum theory method , 2017 .

[2]  S-J Cao,et al.  On the construction and use of linear low-dimensional ventilation models. , 2012, Indoor air.

[3]  Wei-Zhen Lu,et al.  Evaluation of thermal environment by coupling CFD analysis and wireless-sensor measurements of a full-scale room with cooling system , 2019, Sustainable Cities and Society.

[4]  Shugang Wang,et al.  An inverse method based on CFD to quantify the temporal release rate of a continuously released pollutant source , 2013 .

[5]  Yelin Deng,et al.  The effects of ventilation and floor heating systems on the dispersion and deposition of fine particles in an enclosed environment , 2017 .

[6]  Zhuangbo Feng,et al.  Influence of air change rates on indoor CO2 stratification in terms of Richardson number and vorticity , 2018 .

[7]  Prashant Kumar,et al.  Indoor air quality and energy management through real-time sensing in commercial buildings , 2016 .

[8]  Xianting Li,et al.  Building energy efficiency: Passive technology or active technology? , 2017 .

[9]  Olli Seppänen,et al.  Ventilation strategies for good indoor air quality and energy efficiency , 2007 .

[10]  Andrew K. Persily,et al.  Indoor air quality in sustainable, energy efficient buildings , 2011 .

[11]  Yelin Deng,et al.  Impact of ventilation rates on indoor thermal comfort and energy efficiency of ground-source heat pump system , 2018 .

[12]  Cheuk Ming Mak,et al.  Short-term mechanical ventilation of air-conditioned residential buildings: A general design framework and guidelines , 2016 .

[13]  Shu-Hsien Liao,et al.  Artificial neural networks classification and clustering of methodologies and applications - literature analysis from 1995 to 2005 , 2007, Expert Syst. Appl..

[14]  Baskar Ganapathysubramanian,et al.  A methodology for optimal placement of sensors in enclosed environments: A dynamical systems approach , 2016, Building and Environment.

[15]  Lei Chen,et al.  Influence of opening area ratio on natural ventilation in city tunnel under block transportation , 2015 .

[16]  Kamel Ghali,et al.  Optimal control strategy for a multi-zone air conditioning system using a genetic algorithm , 2009 .

[17]  Johan Meyers,et al.  CFD for model-based controller development , 2004 .

[18]  Tom Ben-David,et al.  Sensor networks for routine indoor air quality monitoring in buildings: Impacts of placement, accuracy, and number of sensors , 2018 .

[19]  Shi-Jie Cao,et al.  Ventilation control strategy using low-dimensional linear ventilation models and artificial neural network , 2018, Building and Environment.

[20]  João Dias Carrilho,et al.  Towards sustainable, energy-efficient and healthy ventilation strategies in buildings: A review , 2016 .

[21]  Shi-Jie Cao,et al.  Challenges of using CFD simulation for the design and online control of ventilation systems , 2018, Indoor and Built Environment.

[22]  A. Zorpas,et al.  Indoor air quality evaluation of two museums in a subtropical climate conditions , 2016 .