| Title |
Development of Prediction Models of Dressroom Surface Condensation |
| Authors |
Ju, Eun Ji ; Lee, June Hae ; Park, Cheol-Soo ; Yeo, Myoung Souk |
| DOI |
https://doi.org/10.5659/JAIK_SC.2020.36.3.169 |
| Keywords |
Condensation; Moisture Transfer Model; Parameter Estimation; Data-driven; Artificial Neural Network |
| Abstract |
The authors developed a nodal network model that simulates the flow of moist air and the thermal behavior of a target area. The nodal
network model was enhanced using a parameter estimation technique based on the measured temperature, humidity, and schedule data.
However, the nodal model is not good enough for predicting humidity of the target space, having 55.6% of CVRMSE. It is because
re-evaporation effect could not be modeled due to uncertain factors in the field measurement. Hence, a data-driven model was introduced
using an artificial neural network (ANN). It was found that the data-driven model is suitable for predicting the condensation compared to the
nodal model satisfying ASHRAE Guideline with 3.36% of CVRMSE for temprature, relative humidity, and surface temperature on average.
The model will be embedded in automated devices for real-time predictive control, to minimize the risk of surface condensation at dressroom
in an apartment housing. |