TAILIEUCHUNG - Simulation-based short-term model predictive control for HVAC systems of residential houses
In this paper, we propose a simple model predictive control (MPC) scheme for Heating, ventilation, and air conditioning (HVAC) systems in residential houses. Our control scheme utilizes a fitted thermal simulation model for each house to achieve precise prediction of room temperature and energy consumption in each prediction period. | VNU Journal of Science: Comp. Science & Com. Eng., Vol. 35, No. 1 (2019) 11–22 Simulation-based Short-term Model Predictive Control for HVAC Systems of Residential Houses Hoai Son Nguyen1,∗ , Yasuo Tan2 1 2 VNU University of Engineering and Technology, 144 Xuan Thuy, Cau Giay, Hanoi, Vietnam Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi City, Ishikawa, Japan Abstract In this paper, we propose a simple model predictive control (MPC) scheme for Heating, ventilation, and air conditioning (HVAC) systems in residential houses. Our control scheme utilizes a fitted thermal simulation model for each house to achieve precise prediction of room temperature and energy consumption in each prediction period. The set points for each control step of HVAC systems are selected to minimize the amount of energy consumption while maintaining room temperature within a desirable range to satisfy user comfort. Our control system is simple enough to implement in residential houses and is more efficient comparing with rule-based control methods. Received 25 October 2018, Revised 12 December 2018, Accepted 22 December 2018 Keywords: Model predictive control, air conditioning, thermal simulation. 1. Introduction Smart home services such as air conditioning can bring to us a comfortable living environment, but also consume a large portion of electrical energy. Nowadays, the introduction of a CPS system for smart homes, which includes renewable energy sources, networked appliances and sensors gives us the ability to increase energy efficiency in houses[2]. Environment data gathered by sensor networks, such as temperature, humidity, solar radiation can be used for predicting the dynamic change of system state and optimizing the operation of HVAC systems. This control method is called model predictive control (MPC). MPC control strategies for HVAC systems can adapt more properly to the dynamics of thermal environment than conventional control methods such as .
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