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

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. 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 R 2 equal to 0.9999, with a minimum deviation of 0.0691 (KJ/s), outperforms the ANFIS-GA and single ANFIS models.

COVID-19 Outbreak Prediction with Machine Learning

“…These algorithms use “Potential Solutions” or “Candidate Solutions” or “Possible Hypotheses” for a specific problem in a “chromosome-like” data structure. GA maintains vital information stored in these chromosome data structures by applying “Recombination Operators” to chromosome-like data structures [50-53]. In many cases, GAs are employed as “Function Optimizer” algorithms, which are algorithms used to optimize “Objective Functions.” Of course, the range of applications that use the GA to solve problems is very wide [52,54].…”

“…These ML methods are limited to the basic methods of random forest, neural networks, Bayesian networks, Naïve Bayes, genetic programming and classification and regression tree (CART). Although ML has long been established as a standard tool for modeling natural disasters and weather forecasting [44,45], its application in modeling outbreak is still in the early stages. More sophisticated ML methods (e.g., hybrids, ensembles) are yet to be explored.…”