Abstract
Ventilation, heating and air conditioning systems are the main energy consumers in building sector. Improving the energy consumption of these systems, while satisfying the occupants’ comfort, is the major concern of control and automation designers and researchers. Model predictive control (MPC) methods have been widely studied in order to reduce the energy usage while enhancing the occupants’ comfort. In this paper, a generalized predictive control (GPC) algorithm based on controlled auto-regressive integrated moving average is investigated for standalone ventilation systems’ control. A building’s ventilation system is first modeled together with the GPC and MPC controllers. Simulations have been conducted for validation purposes and are structured into two main parts. In the first part, we compare the MPC with two traditional controllers, while the second part is dedicated to the comparison of the MPC against the GPC controller. Simulation results show the effectiveness of the GPC in reducing the energy consumption by about 4.34% while providing significant indoor air quality improvement.
Highlights
Heating, ventilation and air-conditioning (HVAC) systems represent approximately 50% of the global energy consumption in buildings and 36% of all energy-related CO2 emissions worldwide [1,2].building’s systems, especially HVAC, have to be efficiently controlled in order to balance the tradeoff between the occupants’ comfort and energy efficiency in buildings [3,4,5]
Matlab/Simulink was used as a platform for computing and implementing the Model predictive control (MPC) and generalized predictive control (GPC) models
The Proportional Integral (PI) and state feedback controllers were used as baseline references to investigate the effectiveness of predictive control approaches
Summary
Ventilation and air-conditioning (HVAC) systems represent approximately 50% of the global energy consumption in buildings and 36% of all energy-related CO2 emissions worldwide [1,2]. Advanced algorithms were recently developed in recent years for HVAC control systems, such as the fuzzy logic control, genetic algorithm and model predictive control (MPC) [23,24] Among these control algorithms, predictive control strategies have been introduced as one of the most advanced control techniques used in building system control in order to regulate very complex related processes, such as in HVAC systems [25,26]—especially for energy and cost savings [27,28], robustness to disturbances and changes in operating conditions [29,30], indoor air quality and thermal comfort improvement [31,32].
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