1. Introduction

In China, buildings are responsible for 28% of the total energy consumption [1]. In commercial buildings, heating, ventilation, and air conditioning (HVAC) system accounts for more than 60% building electricity consumption to meet the demand of occupants' comfort. Therefore, a potential benefit can be achieved and environmental impacts can be reduced through energy saving in building HVAC systems. Compared to HVAC equipment update in existing buildings, the improvement of HVAC control strategies is considered as one of the efficient and cost effective ways to increase the building energy efficiency [2].

Although traditional control methods which include the proportional-integral-derivative (PID) control and the ON/OFF control are still the most commonly used control methods in most commercial buildings, these methods have drawbacks in that system level characteristics and multiple interactions among different components are not considered. In recent years, MPC has witnessed a rapid development in the HVAC optimal control [3]. Khanmirza et al., presented a comparison of different control method for a building hybrid heating system, and proved that maximum cost reduction can be achieved while indoor temperature requirement can be met by implementing the MPC method with economical optimization [4]. Salakij et al., developed a MPC method to incorporate critical building information into control algorithms based on a lower order system model modified from coupled system model [5,6]. Through simulation and experiment validation, they demonstrated that the proposed MPC method yielded superior control performance with lowest energy consumption while maintaining indoor thermal comfort.

Although there are extensive researches so far on the development of MPC for increasing energy performance in buildings, there are only a few real practice examples have been reported from literature. Huang et al., implemented MPC in an airport terminal building. The simulation and field experiment demonstrate that energy saving can be achieved without losing thermal comfort after using the proposed hybrid model predictive control [7]. Ruano et al., introduced the implementation of an intelligent model based predictive control (IMBPC) system which consists of the software and hardware in a real university campus building [8]. Through the experiment for almost one month, HVAC electrical saving can be reached and thermal comfort is also maintained by using the IMBPC.

MPC achieves higher control quality compared with PID or ON/OFF control due to its three features including: predictive model, rolling optimization and feedback correction [9,10]. The procedure of modeling is a critical prerequisite for a successful implementation of MPC in buildings [11], because a control process will react to the prediction of input changes or other disturbances [12]. Modeling can be pided into physical-based modeling (or white box modeling) method and datadriven based modeling (or black box modeling) method. Recently, datadriven modeling methods have been widely utilized to model HVAC systems for their advantages on cost-effectiveness and flexibility [13]. Artificial neural network (ANN) is one of widely employed data-driven method in building energy prediction (modeling) due to the ability to deal with nonlinear, multivariable modeling problems. Afram et al., gave a good review on artificial neural network (ANN) based MPC system design [14]. A dynamic temperature setpoints profiles of the zone air and buffer tank can be generated through MPC to save operating cost. Huang et al., developed an ANN model-based system identification method to model multi-zone buildings [15]. The model includes different energy inputs to increase model accuracy for MPC. Ferreira et al., proposed a discrete model-based control method which include three components as predictive models based on ANN, the cost function aiming to minimize energy consumption and maintain thermal comfort and a discrete branch and bound approach for optimization

[16].