A model-based optimal ventilation control strategy of multi-zone VAV air-conditioning systems |
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| Authors: | Xinhua Xu Shengwei Wang Zhongwei Sun Fu Xiao |
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| Affiliation: | 1. University of Almería, Agrifood Campus of International Excellence (ceiA3), CIESOL, Joint Center University of Almería – CIEMAT, Automatic Control, Electronics and Robotics Research Group, Almería, Spain;2. University of Seville, Department of Automation and Systems Technology, Seville, Spain;3. Federal University of Santa Catarina, Department of Automation and Systems, Florianópolis, Brazil;1. Academy of Building Energy Efficiency, School of Civil Engineering, Guangzhou University, Guangzhou 510006, China;2. Department of Civil and Environmental Engineering, Soochow University, Suzhou, 215131, China;1. Center for Green Building and Cities, Graduate School of Design, Harvard University, Cambridge, MA 02138, USA;2. Department of Civil, Architectural and Environmental Engineering, Drexel University, Philadelphia, PA 19104, USA;1. Department of Building Services Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong;2. Research Institute for Sustainable Urban Development, The Hong Kong Polytechnic University, Kowloon, Hong Kong;1. Graduate School, Hanbat National University, San 16-1, Dukmyung-Dong, Yuseong-Gu, Daejeon 305-719, Republic of Korea;2. College of Design, North Carolina State University, 50 Pullen Rd., Campus Box 7701, NC 27695-7701, USA;3. Department of Architecture, Korea University, 145 Anam-ro, Sungbuk-ku, Seoul 02841, Republic of Korea |
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| Abstract: | This paper presents a model-based optimal ventilation control strategy for multi-zone VAV air-conditioning systems aiming at optimizing the total fresh air flow rate by compromising the thermal comfort, indoor air quality and total energy consumption. In this strategy, one scheme is used to correct the total fresh air flow rate dynamically by utilizing the unvitiated fresh air from the over-ventilation zones based on the detected occupancy of each zone and the related measurements. At the meantime, another scheme is developed to optimize the temperature set point for the temperature control of critical zones with the aim at reducing the variation of the required fresh air fractions among all the zones and further reducing the total fresh air intake from outdoors for energy saving when the first scheme is implemented. This scheme is based on a constructed cost function relating thermal comfort, indoor air quality and total energy consumption together while the cost function is calculated based on the prediction of system responses using dynamic simplified models. Genetic algorithm is used for optimizing the temperature set point of critical zones in the optimization process. This strategy was evaluated in a simulated building and air-conditioning environment under various weather conditions. |
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