Addressing these two important issues in HVAC control, two nonlinear control algorithms, a sliding mode controller and an adaptive controller, have been designed based on a simplified HVAC model.
After giving an overview of solution techniques for HVAC system simulation, the paper provides suggestions on how one should select an HVAC modeling approach relative to the simulation objective at hand.
Further, the paper summarizes current approaches used for modeling (i) HVAC components, (ii) HVAC control and (iii) HVAC systems in general.
To that end, HVAC component models with self-tuning parameters were developed and validated in this paper.
The proposed approach is validated by comparing real-time HVAC zone model fitting and prediction against the corresponding experimental measurements.
This paper focuses on real-time HVAC zone model fitting and prediction techniques based on physical principles, as well as the use of genetic algorithms for optimization.
In this study, a HVAC system had been modeled using MATLAB simulation software that had been developed using a fuzzy controlling system and radial basis function (RBF) model of artificial neural network (ANN) as a predictive control system.
The model consisted of the HVAC control system model and a BACnet communication protocol model.
In this paper, an integrated energy model for campus buildings was developed based on a Reduced-Order Model (ROM), which includes building envelope model, and HVAC primary and secondary system models.
This includes consideration of stochastic and transient inputs to the HVAC energy consumption model including local weather, solar loads, driving behavior, charging behavior, and regional passenger fleet population.
As analysis of building energy consumption is facilitated by the accurate prediction of indoor environmental conditions, techniques that dynamically model HVAC zones are crucial, and as such, is an active area of research.
