Modeling of a Building System and its Parameter Identification

This study proposes a low order dynamic model of a building system in order to predict thermal behavior within a building and its energy consumption. The building system includes a thermally well-insulated room and an electric heater. It is modeled by a second order lumped RC thermal network based on the thermal-electrical analogy. In order to identify unknown parameters of the model, an experimental procedure is firstly detailed. Then, the different linear parametric models (ARMA, ARX, ARMAX, BJ, and OE models) are recalled. The parameters of the parametric models are obtained by the least square approach. The obtained parameters are interpreted to the parameters of the physically based model in accordance with their relationship. Afterwards, the obtained models are implemented in Matlab/Simulink® and are evaluated by the mean of the sum of absolute error (MAE) and the mean of the sum of square error (MSE) with the variable of indoor temperature of the room. Quantities of electrical energy and converted thermal energy are also compared. This study will permit a further study on Model Predictive Control adapting to the proposed model in order to reduce energy consumption of the building.

[1]  Yeon-Hee Kim,et al.  Rotor Speed-based Droop of a Wind Generator in a Wind Power Plant for the Virtual Inertial Control , 2013 .

[2]  Siyu Wu,et al.  A physics-based linear parametric model of room temperature in office buildings , 2012 .

[3]  G. Mustafaraj,et al.  Development of room temperature and relative humidity linear parametric models for an open office using BMS data , 2010 .

[4]  Henrik Madsen,et al.  Identifying suitable models for the heat dynamics of buildings , 2011 .

[5]  Peter Crossley,et al.  Design and Evaluation of a Protection Relay for a Wind Generator Based on the Positive- and Negative-Sequence Fault Components , 2013 .

[6]  Tao Lu,et al.  Prediction of indoor temperature and relative humidity using neural network models: model comparison , 2009, Neural Computing and Applications.

[7]  B. Binder,et al.  A simple method to determine the specific heat capacity of thermal insulations used in building construction , 2003 .

[8]  Eric Monmasson,et al.  Thermal parameter identification of simplified building model with electric appliance , 2011, 11th International Conference on Electrical Power Quality and Utilisation.

[9]  Ion Hazyuk.  Dynamical optimisation of renewable energy flux in buildings , 2011 .

[10]  Shengwei Wang,et al.  Transient heat flow calculation for multilayer constructions using a frequency-domain regression method , 2003 .

[11]  Christian Inard,et al.  Fast simulation of temperature distribution in air conditioned rooms by using proper orthogonal decomposition , 2009 .

[12]  Andreas K. Athienitis,et al.  Predictive control of radiant floor heating and solar-source heat pump operation in a solar house , 2011 .

[13]  G. J. Rios-Moreno,et al.  Modelling temperature in intelligent buildings by means of autoregressive models , 2007 .

[14]  Shengwei Wang,et al.  Simplified building model for transient thermal performance estimation using GA-based parameter identification , 2006 .

[15]  Vittorio Ferraro,et al.  A method for the experimental evaluation in situ of the wall conductance , 2006 .

[16]  P.P.J. van den Bosch,et al.  Modeling, identification, and simulation of dynamical systems , 1994 .