Comparative Analysis of Single and Hybrid Neuro-Fuzzy-Based Models for an Industrial Heating Ventilation and Air Conditioning Control System

EasyChair Preprint 2763

Abstract

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 control system. 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.

Keyphrases: ANFIS model, ANFIS-GA, ANFIS-PSO, Adaptive Neuro-Fuzzy Inference System, Air conditioning, Artificial Neural Network, HVAC, HVAC control system, HVAC system, Hybrid Machine learning model, Model Predictive Control, Particle Swarm Optimization, Schematic representation, Soft computing technique, control system, electrical engineering budapest, energy consumption, exergy destruction, fuzzy inference system particle, high performance control system, hybrid machine learning, kalman kando faculty, machine learning, machine learning method, machine learning technique, neural network, prediction model, regia technical faculty obuda, technical faculty obuda university, thermal comfort

@booklet{EasyChair:2763,
  author    = {Sina Ardabili and Bertalan Beszédes and László Nádai and Károly Széll and Amir Mosavi and Imre Felde},
  title     = {Comparative Analysis of Single and Hybrid Neuro-Fuzzy-Based Models for an Industrial Heating Ventilation and Air Conditioning Control System},
  howpublished = {EasyChair Preprint 2763},
  year      = {EasyChair, 2020}}