An intelligent HVAC control strategy for supplying comfortable and energy-efficient school environment |
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| Affiliation: | 1. School of Civil, Environmental and Architectural Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea;2. School of Architecture and Building Science, Chung-Ang University, Seoul 06974, Republic of Korea;1. State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China;2. School of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China;3. National NC System Engineering Research Center, Huazhong University of Science and Technology, Wuhan 430074, China;1. School of Civil, Environmental and Architectural Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea;2. Global Manufacturing & Infrastructure Department, Samsung Electronics Co., Ltd., 1, Samsungjeonja-ro, Hwaseong-si, Gyeonggi-do, Republic of Korea;3. Urban Energy Systems Laboratory, Empa, Überlandstrasse 129, Dübendorf 8600, Switzerland;1. State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300350, China;2. Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong, China;1. School of Reliability and Systems Engineering, Beijing University of Aeronautics and Astronautics, Beijing, PR China;2. Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing, PR China;3. State Key Laboratory of Virtual Reality Technology and System, Beijing, PR China;1. School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China;2. Beijing Xinghang Mechanical-Electrical Eqiupment Co., Ltd., Beijing 100074, China;3. AVIC Manufacturing Technology Institute, Beijing 100024, China;4. School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, China;1. School of Civil, Environmental and Architectural Engineering, College of Engineering, Korea University, 145 Anam–Ro, Seongbuk–Gu, Seoul 02841, Republic of Korea;2. Research Future Construction Environment Convergence Research Institute, College of Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea |
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| Abstract: | In South Korea, school buildings require significant energy inputs for heating and air-conditioning, and the majority of the occupants are adolescent students, whose health and cognitive performance are vulnerable to poor indoor air quality (IAQ) and thermal discomfort. Using field measurements, some previous studies have reported that some Korean schools have poor IAQ and thermal conditions. Thus, it is necessary to develop effective heating, ventilation, and air-conditioning (HVAC) control strategies to improve the indoor environment and reduce energy consumption. Therefore, this study proposes an intelligent HVAC integrated control strategy that can improve indoor environmental quality (IEQ) and reduce energy consumption in school buildings. The proposed strategy utilizes an integrated neural network prediction model for IEQ and a heuristic method that can optimize control objectives (i.e., the predicted mean vote PMV], carbon dioxide CO2], particulate matter with diameters of 10 and 2.5 μm PM10 and PM2.5, respectively], and HVAC energy consumption). To evaluate the control performance of the proposed strategy, the present study employs two base algorithms (i.e., a rule-based and a non-adaptive control approach) under non-disturbance and forcing disturbance scenarios. The control failure period for PMV is found to be 1.6420% and 9.4773% of the total occupancy period under the non-disturbance and forcing disturbance scenarios, respectively, while CO2 control failure does not occur under either scenario. The control failure periods for PM10 and PM2.5 were 5.1676%, and 7.1844%, respectively, under forcing disturbance. Under the non-disturbance scenario, the proposed strategy consumed 2,467.07 kWh and 870,26 kWh for heating and cooling, respectively, representing 91.1% and 84.08% of that for the rule-based algorithm. The proposed strategy can thus effectively improve the IEQ of a building and has the potential for use in the development of integrated environmental management solutions for buildings. |
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| Keywords: | Building integrated control Indoor environmental quality HVAC energy saving Integrated neural network Multi-objective control Heuristic approach |
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