This study proposes an advanced thermal control method that employs artificial neural network (ANN) models for predictive and adaptive thermal control. Two predictive and adaptive control logic approaches were proposed to simultaneously control indoor temperature and humidity as well as predicted mean vote (PMV) in a residential building. Their thermal performance was analysed and compared with that of non-ANN-based counterparts to evaluate architectural variables such as envelope insulation and building orientation. A numerical computer simulation method was used for the tests after demonstration of its validity based on comparison with results of field measurement. Analysis results revealed that the proposed predictive and adaptive control methods conditioned the indoor temperature, humidity and PMV effectively. The periods during which each thermal factor was in a comfortable range increased, and overshoots and undershoots out of the targeted comfortable ranges were reduced when using the ANN model. The results demonstrate the functionality of the proposed method for variation in architectural variables and that the ANN model has the potential to be successfully applied to building thermal controls.