Optimal temperature control of intermittently heated buildings using Model Predictive Control: Part I – Building modeling

An important performance in building thermal control is to ensure thermal comfort with minimal energy consumption. Model Predictive Control (MPC) is considered to be one of the most suited solutions for this due to its ability to use occupancy schedule and weather forecasts for optimal temperature control. MPC relies on a dynamical model of the building, which is the main difficulty of applying it. Therefore, this paper treats the problems related to building modeling and model parameters identification. A robust model of the building is obtained in two stages: firstly physical knowledge is used to determine the structure of a low-order model, then least squares identification method is applied to find the numerical values of the model parameters. In order to perform the identification usually there are required input/output data records having variations which generally are not accepted in inhabited buildings because of imposed comfort conditions. Also inhabited buildings contain unmeasured disturbance sources which may degrade the identified model quality. Therefore this paper proposes to use detailed building models, implemented in dedicated simulation tools, to generate the required input/output data records instead of measuring them on real buildings. This allows us to apply desired input signals and to eliminate disturbance sources. Additionally, the paper presents a method to identify the nonlinearity existing in building thermal behavior, which permits to represent the building by separated linear and nonlinear blocks. This model representation, used along with the linearization method proposed in Part II, permits to design the temperature controller without resorting to the nonlinear system theory.

► The building is modeled by a low-order model for the purpose of the control. ► The model structure is obtained via equivalent electrical circuit. ► The parameters are identified by applying least squares identification method. ► The test building used for application is emulated by a detailed white-box model. ► A nonlinear static characteristic is identified to be used for model linearization.