Event Title
Modeling and model calibration for model predictive occupants comfort control in buildings
Conference Editor
Jianshun Zhang; Edward Bogucz; Cliff Davidson; Elizabeth Krietmeyer
Keywords:
Model predictive control; modeling; model calibration; linearization.
Location
Syracuse, NY
Event Website
http://ibpc2018.org/
Start Date
25-9-2018 1:30 PM
End Date
25-9-2018 3:00 PM
Description
Mathematical models are essential in Model-Predictive Control (MPC) for building automation and control (BAC) application, which must be precise and computationally efficient for realtime optimization and control. However, building models are of high complexity because of the nonlinearities of heat and mass transfer processes in buildings and their air-conditioning and mechanical ventilation (ACMV) systems. This paper proposes a method to develop an integrated linear model for indoor air temperature, humidity and Predicted Mean Vote (PMV) index suitable for fast real-time multiple objectives optimization. A linear dynamic model is developed using SIMSCAPE language based on the BCA SkyLab test bed facility in Singapore as a case study. Experimental data is used to calibrate the model using trust-region-reflective least squares optimization method. The results show that the mean absolute percentage errors (MAPE) of predicted room temperature and humidity ratio are 1.25% and 4.98%, compared to measurement, respectively.
Recommended Citation
Yang, Shiyu; Wan, Man Pun; Ng, Bing Feng; Zhang, Zhe; Lamano, Adrian S.; and Chen, Wanyu, "Modeling and model calibration for model predictive occupants comfort control in buildings" (2018). International Building Physics Conference 2018. 3.
DOI
https://doi.org/10.14305/ibpc.2018.ms-6.03
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Modeling and model calibration for model predictive occupants comfort control in buildings
Syracuse, NY
Mathematical models are essential in Model-Predictive Control (MPC) for building automation and control (BAC) application, which must be precise and computationally efficient for realtime optimization and control. However, building models are of high complexity because of the nonlinearities of heat and mass transfer processes in buildings and their air-conditioning and mechanical ventilation (ACMV) systems. This paper proposes a method to develop an integrated linear model for indoor air temperature, humidity and Predicted Mean Vote (PMV) index suitable for fast real-time multiple objectives optimization. A linear dynamic model is developed using SIMSCAPE language based on the BCA SkyLab test bed facility in Singapore as a case study. Experimental data is used to calibrate the model using trust-region-reflective least squares optimization method. The results show that the mean absolute percentage errors (MAPE) of predicted room temperature and humidity ratio are 1.25% and 4.98%, compared to measurement, respectively.
https://surface.syr.edu/ibpc/2018/MS6/3
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