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Session Chair: Dr. Jiyun Song, University of Hong Kong
Location:Room 2 - Room 011, Building: 116
10:30am - 10:45am
Evaluation and improvement of two-node bioheat model for young subjects
Lili Ji1, Abdelaziz Laouadi2, Chang Shu1, Liangzhu Wang1, Michael Lacasse2
1Department of Building, Civil and Environmental Engineering, Concordia University, Montreal, Canada; 2Construction Research Centre, National Research Council Canada, Ottawa, Canada
Bioheat modelling is an important tool to evaluate the human body’s dynamic physiological response to environmental hot or cold exposures. Under heat/cold stressful conditions, human thermal response prediction can help to avoid extreme thermal disorders and heat/cold-related injuries. In previous studies, many physiological models have been proposed to improve the bioheat modelling. However, there is a lack of evaluation of the advantages and disadvantages of those models for sweating, evaporative efficiency, skin blood flow and shivering. In this study, thermoregulatory models developed in recent years have been implemented in the two-node model and evaluated by a model inter-comparison approach. Under two heat exposure scenarios and three cold exposure scenarios, 63 models have been simulated, and the results of core temperature, skin temperature, evaporative heat loss or water loss, skin blood flow have been compared with experimental data. The models with the best performance have been selected and further optimized. Based on the evaluation results, the two-node model has been improved in terms of the calculation of sweating rate, skin blood flow rate and shivering rate. The proposed model has been validated under both heat and cold exposure conditions with high-quality experimental data and compared with the results of the Predicted Heat Strain (PHS) model and the multi-node models. The improved two-node model can predict the human thermal response more accurately under both heat and cold environment.
10:45am - 11:00am
Improving the energy efficiency of an office building by applying a thermal comfort model
Gratien Jesugo Dieudonné Kiki1,2, Philippe André1, Aristide Houngan2, Clément Kouchadé2
1Building Energy Monitoring and Simulation, Liege University, 6700 Arlon, Belgium; 2Laboratoire d’Energétique et de Mécanique Appliquée, University of Abomey-Calavi, 01 BP 2009 Cotonou, Bénin
The building represents one of the main actors of global warming of the planet because of the significant amounts of energy consumed. In Benin, of electrical energy is consumed by office and service buildings. This is explained by the excessive use of air conditioning systems due to the lack of a thermal comfort index specific to the region. This work therefore focuses on assessing the impact of the choice of a thermal comfort model on the energy efficiency of buildings. For this purpose, an office building was chosen in the south of Benin and comfort surveys were conducted among the occupants. The model selected for this purpose is the adaptive model developed by López-Pérez and al. for air-conditioned buildings in humid tropical regions. Subsequently, a monitoring campaign of meteorological, hygrothermal and energetic data of the building was carried out during six months. The results obtained show that the average temperature of the offices () during the hours of occupancy is relatively lower than the comfort temperature determined with the model (). Moreover, the different simulations carried out under TRNSYS by substituting the office temperatures by the comfort temperature show a reduction of about of the building's energy consumption. This shows the importance of the comfort model of López-Pérez and al. in improving the energy efficiency of the building.
11:00am - 11:15am
An Event-Triggered Model Predictive Control for Energy Efficiency and Thermal Comfort Optimization in Buildings
Shiyu Yang, Wanyu Chen, Man Pun Wan
Nanyang Technological University, Singapore
Model predictive control (MPC) for building automation has been demonstrated capable of achieving substantial energy savings and significantly improved human comfort. However, the high requirement of computing power to solve the optimization is challenging the implementation of MPC for real-time building control. One reason leading to the issue is the time-triggered mechanism (TTM) employed by MPC, which triggers the optimization actions periodically at each control interval. Although each optimization action generates a series of control commands in a prediction horizon, only the control command at the first control interval is applied to buildings, and the remainings are discarded. This study proposes an event-triggered mechanism (ETM) for MPC, which triggers the optimization action only when an event is detected. Different from conventional ETM that is based on the measured past/current information, the ETM proposed in this study is based the cost function considering both the measured past/current and the predicted future information. After one optimization action is executed, at each control interval, the ETM re-evaluates the cost function for the prediction horizon using the measured past/current building states and the building model. Then the ETM compares the re-evaluated cost to the cost initially evaluated by the last optimization action. The ETM only triggers the optimization action if the error between the two costs is beyond a threshold or all the control commands generated by the last optimization action have been applied to the building. An event-triggered model predictive control (ETMPC) for optimizing both building energy efficiency and thermal comfort is developed using the proposed ETM. The ETMPC is then used to control an air-conditioning system of a test building through simulations for performance evaluation. Compared to a MPC with TTM, ETMPC could significantly reduce the computation load by 80% with ony minor degradation in the energy saving and thermal comfort performance.