As for physical-based method, it can provide detailed building information based on the analysis of building material features. So some researchers proposed that rigorous model or physical model should be implemented [17–19], but it is seldom employed in real engineering practice for the high costs and expert involvement. Furthermore some key parameters such as the heat transfer coefficient and specific heat capacity of building envelope cannot be easily obtained, especially in large size commercial buildings, and when there exists high couples among different components in a system. Fortunately, some studies show that a simplified model can achieve acceptable accuracy in comparison to a detailed physical model for control [20–23]. Sourbron et al., established a second-order model and a fourth-order model, which included additional solar and internal gains in the identification data set to improve performance as contrast to the second-order one, for concrete core activation (CCA) [20]. The result indicated that both second-order and forth-order model can achieve a satisfactory control performance. Bălan et al., demonstrated a simple solution with a second-order model for thermal modeling of a house which included experimental identification of the model's parameters. Based the model, they successfully used a predictive control algorithm to control the thermal system in a house [21]. Karlsson et al., established a simplified two-node lumped model for a single room [22]. Prívara et al., identified a real building model by using subspace methods and successfully applied it into real control [23].

Similar to these low-order models stated above, resistance and capacitance (RC) network models or inverse models are widely used for building energy estimation [24,25]. RC network modeling possesses a meaningful and transparent configuration, making the developed model more understandable and reliable to use [7]. Since the models established by the RC method or some simplified methods are all linear models, it is actually already using a priori knowledge – the system can be approximated as a linear system, when using these methods. As for large sized buildings, state-space modeling approach based on RC method can also be applied [26], although sometime this approach are applied in a single zone building model for details [25]. In practice, a large sized building includes many zones, rather than two zones as in Ref. [26]. Many zones will produce a high dimension matrix of the state-space which results in great difficulties in practical application [43]. For the purpose of chiller plant control, it is not necessary to model all zones in the building. Moreover, the structures and parameters of a RC model obtained from one building can hardly be employed in other building. This makes the scalability of a RC model to be lower in the real practice [11]. Therefore, some simplified methods have been proposed to cope with this issue. For example, Braun et al., modeled a whole large sized building as a single zone instead of developing a multi-zone RC model [25]. The result indicated that there was a close match between the predicted cooling load and the actual cooling load. Sourbron et al., also demonstrated that the second-order model can achieve equal control performance as the forth-order one [20]. Richard et al. proved that it is feasible to simplify high order systems to low order ones in engineering applications [12,27,28].

The system parameters identification (SPID) is another challenge after the determination of system structure. Various SPID methods have been proposed to optimize model parameters. O'Dwyer et al., developed a system identification method from data in which significant unmeasured disturbances are present [29]. In this method, high-order simulation models for control strategy analysis and low-order zone models for optimization were developed separately for MPC. Karlsson et al., identified the parameters by using step-response method and pulse-response method [22]. It is effective to model the indoor temperature influenced by the supply heat flux, since the supply heat flux can be a step change or an approximate ideal impulse function. However, these methods that are called experimental methods can only be applied to variables that can be manipulated (e.g. the supply heat flux, the supply chilled water temperature), and can't applied to random ones (e.g. the outdoor temperature, the solar radiation). By using simulation, Fraisse et al., studied the internal surface temperature response after a step change in external or internal temperature [30]. Yao et al., also studied the dynamic response of air temperature in different zones when subject to a step change of ambient temperature, occupant number and etc. [31]. However, some weather variables such as outdoor temperature, solar radiation, cannot be step changed in real practice.