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.

Comments

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DOI

https://doi.org/10.14305/ibpc.2018.ms-6.03

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 4.0 License.

COinS
 
Sep 25th, 1:30 PM Sep 25th, 3:00 PM

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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