A heating, ventilation, and air-conditioning (HVAC) system is a multi-variable strongly coupled largescale system that is composed of several sub-systems. Considerable research, simulations, and experiments have been conducted on HVAC control. The optimization control of an HVAC system is now the popular issue. The ultimate goal of this paper was to achieve minimum energy consumption and improve system efficiency. Multi-zone variable air volume and variable water volume air-conditioning systems were developed. The dynamic models of HVAC sub-systems were built by the adaptive directional forgetting method. Control strategies such as the gearshift integral proportional-integral-derivative (PID) controller and self-tuning PID controller were studied in the platform to improve the dynamic characteristics of the HVAC system. System performance was improved. The system saved 18.2% of energy with the integration of iterative learning control and sequential quadratic programming based on the steady-state hierarchical optimization control scheme.
The stable operation of the central air conditioning water system always is a major difficulty for the control profession. Paper focus on the water system with multi variable, strong coupling, nonlinear, large time delay characteristics, presented use feed forward coupling compensation method, to eliminate the coupling effect between temperature and pressure. In this paper, the Elman neural network controller is designed for the first time, and the simulation results show that the response time of Elman neural network controller is shorter, the system is more stable and the overshoot is small.
Abstract:The air-conditioning system in a large commercial or high-rise building is a complex multi-variable system influenced by many factors. The energy saving potential from the optimal operation and control of heating, ventilating, and airconditioning (HVAC) systems can be large, even when they are properly designed. The ultimate goal of optimization is to use the minimum amount of energy needed to improve system efficiency while meeting comfort requirements. In this study, a multizone variable air volume (VAV) and variable water volume (VWV) air-conditioning system is developed. The steady state modes and dynamic models of the HVAC subsystems are constructed. Optimal control based on large scale system theory for system-level energy-saving of HVAC is introduced. Control strategies such as proportional-integral-derivative (PID) controller (gearshift integral PID and self-tuning PID) and iterative learning control (ILC) are studied in the platform to improve the dynamic characteristics. The system performance is improved. An 18.2% energy saving is achieved with the integration of ILC and sequential quadratic programming based on a steady-state hierarchical optimization control scheme.
The preheat time of central air-conditioning system preheating space is not only affect the comfort of environment, but also affect energy consumption. A preheat time model is established based on the thermodynamics principle, and an optimum stat-up algorithm of central air-conditioning system is proposed based on the NN(neural network), the simulation result shows that this method can predict the preheat time accurately which make central air-conditioning system save energy.
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