A review of recent developments and technological advancements of variable-air-volume (VAV) air-conditioning systems

This study reviewed VAV systems modeling and simulations, control strategies and optimization tools, the airflow characteristics of VAV systems, some common VAV systems' faults, detection and diagnosis, energy usage and analysis, and the current applications of variable air volume (VAV) air-conditioning systems. VAV system modeling is very complex as it involves complex structures and parameters a result of which has led to lack of models that combine both the AHU and building with all the required parameters. The most common controllers used in VAV systems are the PID controllers. We saw that supply air temperature and the flow rate of supply air are the best parameters that can be optimized in a VAV system. as they greatly minimize energy consumption. Genetic algorithms have good robustness, and, can be easily parallelized. However, they suffer from shortcomings such as slow convergence rates under some conditions, and have difficulty in adjustment of algorithms since there are no rules for determining the number of individuals in populations. FLCs boost of advantages such as less or minimum overshoot, oscillation and power consumption compared to conventional PID controllers, can be used in MIMO systems, and they do not require models as they can control non-linear processes. Airflow control in VAV systems can be achieved through controlling static pressure and position of the damper. Literature survey shows that balancing and distribution of airflow in VAV air-conditioning systems can be considered to be one of the main challenging areas of research concerning VAV system control. Most methods used today for detecting and diagnosing faults are hybrid. These are superior to the conventional methods of FDD. In conclusion, VAV air-conditioning systems are the most energy efficient systems in use today. Despite of their current strengths, VAV systems energy saving potential can still be improved. (C) 2016 Elsevier Ltd. All rights reserved.