Hybridization of machine learning methods with soft computing techniques is
an essential approach to improve the performance of the prediction models.
Hybrid machine learning models, particularly, have gained popularity in the
advancement of the high-performance control systems. Higher accuracy and better
performance for prediction models of exergy destruction and energy consumption
used in the control circuit of heating, ventilation, and air conditioning
(HVAC) systems can be highly economical in the industrial scale to save energy.
This research proposes two hybrid models of adaptive neuro-fuzzy inference
system-particle swarm optimization (ANFIS-PSO), and adaptive neuro-fuzzy
inference system-genetic algorithm (ANFIS-GA) for HVAC. The results are further
compared with the single ANFIS model. The ANFIS-PSO model with the RMSE of
0.0065, MAE of 0.0028, and R2 equal to 0.9999, with a minimum deviation of
0.0691 (KJ/s), outperforms the ANFIS-GA and single ANFIS models.Comment: 6 pages, 6 figure
