A new simplified modeling method for model predictive control in a medium-sized commercial building: A case study. (January 2018)

• Record Type: Journal Article
• Authors: Zhuang, Junhua; Chen, Yimin; Chen, Xiangguang
• Is Part Of: Building and environment. Volume 127(2018)
• Page Start: 1
• Page End: 12
• Publication Date: 2018-01
• Subjects: HVAC -- Power spectral density -- Model predictive control -- Feedforward control structure; Buildings -- Environmental engineering -- Periodicals; Building -- Research -- Periodicals; Constructions -- Technique de l'environnement -- Périodiques; Electronic journals; 696
• DOI: 10.1016/j.buildenv.2017.10.022
• Languages: English
• ISSNs: 0360-1323

Abstract

Abstract: Model predictive control (MPC) methods for heating, ventilation and air conditioning (HVAC) systems have been studied to improve the control accuracy and reduce energy consumption in recent years. The accuracy of the model for building thermal dynamics in MPC plays a critical role to accurately control the system. The modeling method also impacts on the real practice of MPC in buildings due to its cost and scalability. Studies have shown that an appropriate simplification of modeling procedure has minor impacts on the model accuracy, but increases the modeling efficiency. In this article, variables including weather conditions, occupancy and electricity are divided into two categories: manipulated variables and random variables. A novel two-step modeling strategy is proposed for simplifying modeling procedure and increasing model accuracy. Manipulated variables are used in step response method to develop system model. A low order system is obtained after the model simplification by observing the response curve. Random variables are used in the power spectral density (PSD) method for modeling. Transfer function is obtained through calculating the cross-power spectral density (CPSD) of the system output and input, the PSD of the input, and the ratio of CPSD and PSD. A MPC strategy with feedforward control structure is proposed to utilize the obtained dynamic characteristics of random variables and effectively compensate the errors caused by these variables. Field test in a medium-sized commercial building is implemented to evaluate the MPC strategy. The result shows that a considerable amount of energy saving is achieved through the proposed MPC. Highlights: Manipulated variables and random variables are pre-defined for simplifying the process of modeling. PSD method is used to model for random variables. MPC with feed-forward control strategy is put forward for random variables selection. Real medium-sized commercial building is used to evaluate the proposed modeling method.