Version 1
: Received: 22 February 2020 / Approved: 24 February 2020 / Online: 24 February 2020 (01:55:59 CET)
How to cite:
Ardabili, S.; Beszedes, B.; Nadai, L.; Szell, K.; Mosavi, A.; Imre, F. Comparative Analysis of Single and Hybrid Neuro-Fuzzy-Based Models for an Industrial Heating Ventilation and Air Conditioning Control System. Preprints2020, 2020020337. https://doi.org/10.20944/preprints202002.0337.v1
Ardabili, S.; Beszedes, B.; Nadai, L.; Szell, K.; Mosavi, A.; Imre, F. Comparative Analysis of Single and Hybrid Neuro-Fuzzy-Based Models for an Industrial Heating Ventilation and Air Conditioning Control System. Preprints 2020, 2020020337. https://doi.org/10.20944/preprints202002.0337.v1
Ardabili, S.; Beszedes, B.; Nadai, L.; Szell, K.; Mosavi, A.; Imre, F. Comparative Analysis of Single and Hybrid Neuro-Fuzzy-Based Models for an Industrial Heating Ventilation and Air Conditioning Control System. Preprints2020, 2020020337. https://doi.org/10.20944/preprints202002.0337.v1
APA Style
Ardabili, S., Beszedes, B., Nadai, L., Szell, K., Mosavi, A., & Imre, F. (2020). Comparative Analysis of Single and Hybrid Neuro-Fuzzy-Based Models for an Industrial Heating Ventilation and Air Conditioning Control System. Preprints. https://doi.org/10.20944/preprints202002.0337.v1
Chicago/Turabian Style
Ardabili, S., Amir Mosavi and Felde Imre. 2020 "Comparative Analysis of Single and Hybrid Neuro-Fuzzy-Based Models for an Industrial Heating Ventilation and Air Conditioning Control System" Preprints. https://doi.org/10.20944/preprints202002.0337.v1
Abstract
The 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.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
