A state-space thermal model incorporating humidity and thermal comfort for model predictive control in buildings

• Main Authors: Yang, Shiyu, Wan, Man Pun, Ng, Bing Feng, Zhang, Tian, Babu, Sushanth, Zhang, Zhe, Chen, Wanyu, Dubey, Swapnil
• Other Authors: School of Mechanical and Aerospace Engineering
• Format: Article
• Language: English
• Published: 2019
• Subjects: Model Predictive Control; State-space Model; DRNTU::Engineering::Mechanical engineering
• Online Access: https://hdl.handle.net/10356/105751; http://hdl.handle.net/10220/48732
• Institution: Nanyang Technological University

A major challenge in applying Model Predictive Control (MPC) to building automation and control (BAC) is the development of a simplified mathematical model of the building for real-time control with fast response times. However, building models are highly complex due to nonlinearities in heat and mass transfer processes of the building itself and the accompanying air-conditioning and mechanical ventilation systems. This paper proposes a method to develop an integrated state-space model (SSM) for indoor air temperature, radiant temperature, humidity and Predicted Mean Vote (PMV) index suitable for fast real-time multiple objectives optimization. Using the model, a multi-objective MPC controller is developed and its performance is evaluated through a case study on the BCA SkyLab test bed facility in Singapore. The runtime of the MPC controller is less than 0.1 s per optimization, which is suitable for real-time BAC applications. Compared to the conventional ON/OFF control, the MPC controller can achieve up to 19.4% energy savings while keeping the PMV index within the acceptable comfort range. When the MPC controller is adjusted to be thermal-comfort-dominant that achieves a neutral PMV index at most office hours, the system can still bring about 6% in energy savings as compared to the conventional ON/OFF control.