Investigation of Input Feature Combinations Considering Occupant Behavior for Modelling Indoor Air Temperature in a Classroom
Conférence : Communications avec actes dans un congrès international
This study investigated the performance of artificial
neural networks and random forests with various
combinations of input variables in modelling indoor
air temperature in a classroom. The data collection
methodology was designed to investigate key input
parameters, including indoor air data, classroom occupancy,
and occupant behavior factors such as windows,
doors, blind operation, and occupant interaction
with HVAC control. The random forest models
exhibited the highest accuracy (determination coefficient
of 0.99) compared with artificial neural networks
(determination coefficient of 0.80). The results
also show that the occupant behavior data are important
parameters for model performance. Combinations
with limited occupant behavior data exhibited
high root-mean-square errors.