Modeling of a Building System and its Parameter Identification

- Journal title : Journal of Electrical Engineering and Technology
- Volume 8, Issue 5, 2013, pp.975-983
- Publisher : The Korean Institute of Electrical Engineers
- DOI : 10.5370/JEET.2013.8.5.975

Title & Authors

Modeling of a Building System and its Parameter Identification

Park, Herie; Martaj, Nadia; Ruellan, Marie; Bennacer, Rachid; Monmasson, Eric;

Park, Herie; Martaj, Nadia; Ruellan, Marie; Bennacer, Rachid; Monmasson, Eric;

Abstract

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(R) 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.

Keywords

Dynamic model;Parameter identification;Thermal network;Low energy building;

Language

English

Cited by

References

1.

World Business Council for Sustainable Development, Transforming the Market: Energy Efficiency in Buildings, 2009.

2.

European Commission, Multi-Annual Roadmap and Longer Term Strategy, 2010.

3.

H. Park, M. Ruellan, A. Bouvet, E. Monmasson, R. Bennacer, Thermal parameter identification of simplified building model with electric appliance, in Proc. IEEE EPQU, 2011, pp. 1-6.

4.

J.A. Candanedoa, A.K. Athienitisa, Predictive control of radiant floor heating and solar-source heat pump operation in a solar house, HVAC&R Research, Vol. 17, 2011, pp. 235-256

5.

I. Hazyuk, Dynamical optimisation of renewable energy flux in buildings, INSA de Lyon, Ph.D. Thesis, 2010.

6.

A. Sempey, C. Inard, C. Ghiaus, C. Allery, Fast simulation of temperature distribution in air conditioned rooms by using proper orthogonal decomposition, Building and Environment, Vol. 44, 2009, pp. 280-289.

7.

G. J. Rios-Moreno, M. Trejo-Perea, R. Castaneda-Miranda, V. M. Hernandez-Guzman, G. Herrera-Ruiz, Modelling temperature in intelligent buildings by means of autoregressive models, Automation in Construction, Vol. 16, 2007, pp. 713-722.

8.

B. Peder, M. Henrik, Identifying suitable models for the heat dynamics of buildings, Energy and Buildings, Vol. 43, 2011, pp. 1511-1522.

9.

S. Wang, Y. Chen, Transient heat flow calculation for multilayer constructions using a frequency-domain regression method, Building and Environment, Vol. 38, 2003, pp. 45-61.

10.

S. Wang, X. Xu, Simplified building model for transient thermal performance estimation using GAbased parameter identification, International Journal of Thermal Sciences, Vol. 45, 2006, pp. 419-432.

11.

K. Ghazi Wakili, B. Binder, R. Vonbank, A simple method to determine the specific heat capacity of thermal insulations used in building construction, Energy and Buildings, Vol. 35, 2003, pp. 413-415.

12.

M. Cucumo, A. De Rosa, V. Ferraro, D. Kaliakatsos, V. Marinelli, A method for the experimental evaluation in situ of the wall conductance, Energy and Buildings, Vol. 38, 2006, pp. 238-244.

13.

G. Mustafaraj, J. Chen, G. Lowry, Development of room temperature and relative humidity linear parametric models for an open office using BMS data, Energy and Buildings, Vol. 42, 2010, pp. 348-356.

14.

T. Lu, M. Viljanen, Prediction of indoor temperature and relative humidity using neural network models: model comparison, Neural Computing & Applications, Vol. 18, 2009, pp. 345-357.

15.

P. P. J. van den Bosch, A. C. van der Klauw, Modeling, Identification and Simulation of Dynamical Systems, CRC Press, 1994, Ch. 2, 4.

16.

S. Wu, J. Q. Sun, A physics-based linear parametric model of room temperature in office buildings, Building and Environment, Vol. 50, 2012, pp. 1-9.