基于群体热感觉和区域占用率的多区域变风量空调系统优化控制方法
摘要：为优化多区域变风量空调系统的控制性能，同时使室内环境满足人员的热舒适需求，提出了一种基于群体热感觉和区域占用率的多区域变风量空调系统优化控制方法。根据室内人员的群体热感觉和各区域内的人员数量，采用调控算法调整室温设定值，进而调整各控制区域的末端送风量。搭建了仿真平台，对该控制方法与传统控制方法从控制效果、人员热舒适性、空调系统能耗等方面进行了对比分析。研究结果表明：加入群体热感觉后，该控制方法相较于传统控制方法空调系统节能率为7.5%以上；若同时考虑群体热感觉和区域占用率，该控制方法的节能率高于8.5%。
关键词：变风量空调多区域热感觉区域占用率优化控制节能
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[4] CHEN X,WANG Q,SREBRIC J.Occupant feedback based model predictive control for thermal comfort and energy optimization:a chamber experimental evaluation[J].Applied energy,2016,164:341- 351.
[5] HU C C,LI H L.Deducing the classification rules for thermal comfort controls using optimal method[J].Building and environment,2016,98:107- 120.
[6] KIM S H,MOON H J.Case study of an advanced integrated comfort control algorithm with cooling,ventilation,and humidification systems based on occupancy status[J].Building and environment,2018,133:246- 264.
[7] YOON Y R,MOON H J.Performance based thermal comfort control (PTCC) using deep reinforcement learning for space cooling[J].Energy and buildings,2019,203:109420.
[8] 韩尔东，李百战，杜晨秋，等.基于朴素贝叶斯增量学习算法的个体热舒适预测模型[J].暖通空调，2021,51(11):13- 21.
[9] LI W,ZHANG J L,ZHAO T Y.Indoor thermal environment optimal control for thermal comfort and energy saving based on online monitoring of thermal sensation[J].Energy and buildings,2019,197:57- 67.
[10] CHAUDHURI T,SOH Y C,LI H,et al.A feedforward neural network based indoor-climate control framework for thermal comfort and energy saving in buildings[J].Applied energy,2019,248:44- 53.
[11] LI D,MENASSA C C,KAMAT V R,et al.HEAT-human embodied autonomous thermostat[J].Building and environment,2020,178:106879.
[12] ZHU X,SHI T T,JIN X Q,et al.Multi-sensor information fusion based control for VAV systems using thermal comfort constraints[J].Building simulation,2021,14(4):1047- 1062.
[13] NIKDEL L,JANOYAN K,BIRD S D,et al.Multiple perspectives of the value of occupancy-based HVAC control systems[J].Building and environment,2018,129:15- 25.
[14] ANASTASIADI C,DOUNIS A I.Co-simulation of fuzzy control in buildings and the HVAC system using BCVTB[J].Advances in building energy research,2018,12(2):195- 216.
[15] DONG J,WINSTEAD C,NUTARO J,et al.Occupancy-based HVAC control with short-term occupancy prediction algorithms for energy-efficient buildings[J].Energies,2018,11(9):1- 20.
[16] SOUDARI M,KAPARIN V,SRINIVASAN S,et al.Predictive smart thermostat controller for heating,ventilation,and air-conditioning systems[J].Proceedings of the Estonian Academy of Sciences,2018,67(3):291- 299.
[17] ANAND P,SEKHAR C,CHEONG D,et al.Occupancy-based zone-level VAV system control implications on thermal comfort,ventilation,indoor air quality and building energy efficiency[J].Energy and buildings,2019,204:109473.
[18] TURLEY C,JACOBY M,PAVLAK G,et al.Development and evaluation of occupancy-aware HVAC control for residential building energy efficiency and occupant comfort[J].Energies,2020,13(20):5396.
[19] 李威，张吉礼，赵天怡.群体热感觉模糊综合评判方法研究[J].暖通空调，2020,50(9):122- 127.
[20] 李威，张吉礼，赵天怡.基于人体热感觉穿戴传感的室内温度优化调控方法研究[J].暖通空调，2019,49(11):26- 31.
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Optimal control method for multi-area VAV air conditioning systems based on group thermal sensation and area occupancy rate
Abstract: To optimize the control performance of multi-area variable air volume(VAV) air conditioning systems and make the indoor environment meet the thermal comfort needs of personnel, an optimal control method for multi-area VAV air conditioning systems based on group thermal sensation and area occupancy rate is proposed. According to the group thermal sensation of indoor personnel and the number of personnel in each area, the control algorithm is used to adjust the setting value of room temperature, and then adjust the terminal air supply volume of each controlled area. A simulation platform is built, and the proposed control method is compared with the traditional control method from the aspects of control effect, thermal comfort of personnel and energy consumption of air conditioning systems. The results show that after adding the group thermal sensation, the energy-saving rate of this control method is more than 7.5% compared with that of the traditional control method. If both the group thermal sensation and the area occupancy rate are taken into account, the energy-saving rate of this control method is higher than 8.5%.
Keywords: variable air volume; air conditioning; multi area; thermal sensation; area occupancy rate; optimal control; energy saving;
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