Neuro-optimal operation of a variable air volume HVAC&R system |
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| Authors: | Min Ning M Zaheeruddin |
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| Affiliation: | 1. Faculty of Science and Technology, Free University of Bozen-Bolzano, Italy;2. Department of Design and Planning in Complex Environments, University IUAV of Venice, Italy;1. Aalborg University, Danish Building Research Institute, A.C. Meyers vænge 15, 2450 Copenhagen SV, Denmark;2. KTH Royal Institute of Technology, Fluid and Climate Technology, Brinellvägen 23, SE-100 44 Stockholm, Sweden;1. Tianjin Key Laboratory of Indoor Air Environmental Quality Control, Key Laboratory of Efficient Utilization of Low and Medium Grade Energy, School of Environmental Science and Engineering, Tianjin University, Tianjin, China;2. Qingdao Tengyuan Design Institute Co., Ltd., Qingdao, China;3. School of Architecture, Tianjin University, Tianjin, China;4. Lawrence Berkeley National Laboratory, Berkeley, CA, USA;5. School of Architecture and Built Environment, The University of Adelaide, Adelaide, Australia;1. School of Automation & Electronic Engineering, Qingdao University of Science & Technology, Qingdao 266061, Shandong, China;2. Shandong Provincial Key Laboratory of Intelligent Buildings Technology, Jinan 250101, China;3. Department of Basic Courses, Shandong Institute of Commerce and Technology, Jinan 250103, Shandong, China;4. School of Information Science and Engineering, Shandong Normal University, Jinan, Shandong 250014, China;1. School of Energy Science and Engineering, Central South University, Changsha 410083, China;2. Institute of Refrigeration and Cryogenics, Zhejiang University, Hangzhou, China;3. School of Engineering and Technology, Central Queensland University, Cairns, QLD 4870, Australia;4. School of Architecture and Traffic Engineering, Guilin University of Electronic Technology, Guangxi 541004, China |
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| Abstract: | Low operational efficiency especially under partial load conditions and poor control are some reasons for high energy consumption of heating, ventilation, air conditioning and refrigeration (HVAC&R) systems. To improve energy efficiency, HVAC&R systems should be efficiently operated to maintain a desired indoor environment under dynamic ambient and indoor conditions. This study proposes a neural network based optimal supervisory operation strategy to find the optimal set points for chilled water supply temperature, discharge air temperature and VAV system fan static pressure such that the indoor environment is maintained with the least chiller and fan energy consumption. To achieve this objective, a dynamic system model is developed first to simulate the system behavior under different control schemes and operating conditions. A multi-layer feed forward neural network is constructed and trained in unsupervised mode to minimize the cost function which is comprised of overall energy cost and penalty cost when one or more constraints are violated. After training, the network is implemented as a supervisory controller to compute the optimal settings for the system. Simulation results show that compared to the conventional night reset operation scheme, the optimal operation scheme saves around 10% energy under full load condition and 19% energy under partial load conditions. |
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