کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
264011 | 504087 | 2012 | 9 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Neural network based prediction method for preventing condensation in chilled ceiling systems Neural network based prediction method for preventing condensation in chilled ceiling systems](/preview/png/264011.png)
Condensation is prone to occur at the startup moment in chilled ceiling systems, due to the infiltration and accumulation of moisture during system-off. To prevent condensation, an effective method is to operate the dedicated outdoor air system (DOAS) to dehumidify indoor air before operating chilled ceiling system. The pre-dehumidification time is critical. However, there is little experience in determining the pre-dehumidification time in both research and practice. In this study, neural network (NN) is used to predict condensation risk and the optimal pre-dehumidification time in chilled ceiling systems. Two NN models are developed to predict the temperature on the surface of chilled ceiling and indoor air dew-point temperature at the startup moment so as to evaluate the risk of condensation. The third NN model is developed to predict the optimal pre-humidification time for condensation prevention. Both training data and validation data are obtained from simulation tests in TRNSYS. The results show that 30 min pre-dehumidification is sufficient for the simulated building in Hong Kong. The influence of infiltration rate on the pre-dehumidification time is also investigated. This study also shows that NN-based method can be used for predictive control for condensation prevention in chilled ceiling systems.
► Condensation is prone to occur at the startup moment of chilled ceiling systems.
► Pre-dehumidification time is critical for condensation prevention and energy usage.
► Two neural network (NN) models are developed to evaluate the condensation risk.
► The third NN model is developed to predict the optimal pre-dehumidification time.
► NN method is reliable for predictive condensation control in chilled ceiling systems.
Journal: Energy and Buildings - Volume 45, February 2012, Pages 290–298