Indoor-environment simulator for control design purposes

Building-management systems (BMSs) are becoming increasingly important as they are an efficient means to having buildings that consume less energy as well as for improving the indoor working and living environments. On the other hand, implementing automated control and monitoring systems in buildings is still relatively new, and one of the obstacles for their wider implementation is the ease of setting up the appropriate parameters for the controllers. During our work on an experimental controller for an indoor environment that is installed in an occupied office in the building of the Faculty of Civil and Geodetic Engineering, University of Ljubljana, Slovenia, it has become evident that a computer simulator of the system would be a welcome aid for the optimization of its functioning. In this paper we present a simulator application developed in a combined Matlab/Simulink and Dymola/Modelica environment. The simulator mirrors the functioning of the control system and the dynamics of the indoor environment, where the thermal model of the simulator was developed in the Dymola/Modelica environment, while the illuminance model was developed and parameterized as a black-box model on the basis of measurements in the Matlab environment. The simulator can emulate the response of conventional ON/OFF controllers as well as fuzzy controllers. The paper presents the design of the simulator with all of the key elements described. The underlying models for the thermal and illuminance control are also separately described. Finally, the performance of the simulator is presented for a selected day.

[1]  Guanrong Chen,et al.  Necessary Conditions for Some Typical Fuzzy Systems as Universal Approximators , 1997, Autom..

[2]  Mateja Dovjak,et al.  Automatically controlled daylighting for visual and non-visual effects , 2011 .

[3]  Cheng-Jian Lin,et al.  SISO Nonlinear System Identification Using a Fuzzy-Neural Hybrid System , 1997, Int. J. Neural Syst..

[4]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[5]  Jørn Toftum,et al.  Central automatic control or distributed occupant control for better indoor environment quality in the future , 2010 .

[6]  L X Wang,et al.  Fuzzy basis functions, universal approximation, and orthogonal least-squares learning , 1992, IEEE Trans. Neural Networks.

[7]  Francesco Borrelli,et al.  A distributed predictive control approach to building temperature regulation , 2011, Proceedings of the 2011 American Control Conference.

[8]  R. Goldberg.  Methods of Real Analysis , 1964 .

[9]  Igor Škrjanc,et al.  Theoretical and experimental FUZZY modelling of building thermal dynamic response , 2001 .

[10]  Christian Ghiaus,et al.  Optimal temperature control of intermittently heated buildings using Model Predictive Control: Part I – Building modeling , 2012 .

[11]  Manuel R. Arahal,et al.  Neural network and polynomial approximated thermal comfort models for HVAC systems , 2013 .

[12]  Murat Kunt,et al.  ÉCOLE POLYTECHNIQUE FÉDÉRALE DE LAUSANNE , 2000 .

[13]  Antoine Guillemin.  Using genetic algorithms to take into account user wishes in an advanced building control system , 2003 .

[14]  Anton Sodja,et al.  Modelling thermal processes in buildings using an object-oriented approach and Modelica , 2009, Simul. Model. Pract. Theory.

[15]  Borut Zupančič,et al.  Daylight illuminance control with fuzzy logic , 2006 .

[16]  Stephen Selkowitz,et al.  Integrating automated shading and smart glazings with daylight controls , 2004 .

[17]  Frauke Oldewurtel,et al.  Experimental analysis of model predictive control for an energy efficient building heating system , 2011 .

[18]  Alesš Krainer.  Passivhaus contra bioclimatic design , 2008 .

[19]  Michaël Kummert,et al.  A neural network controller for hydronic heating systems of solar buildings , 2004, Neural Networks.

[20]  Sean Hay Kim,et al.  Building demand-side control using thermal energy storage under uncertainty: An adaptive Multiple Model-based Predictive Control (MMPC) approach , 2013 .

[21]  Aleš Krainer,et al.  Fuzzy control system for thermal and visual comfort in building , 2008 .

[22]  Bart Kosko,et al.  Fuzzy Systems as Universal Approximators , 1994, IEEE Trans. Computers.

[23]  Christian Ghiaus,et al.  Optimal temperature control of intermittently heated buildings using Model Predictive Control: Part , 2012 .

[24]  Mateja Dovjak,et al.  Alternative to the Conventional Heating and Cooling Systems in Public Buildings , 2010 .

[25]  Francisco Rodríguez,et al.  A comparison of thermal comfort predictive control strategies , 2011 .

[26]  D. Kolokotsa,et al.  Advanced fuzzy logic controllers design and evaluation for buildings’ occupants thermal–visual comfort and indoor air quality satisfaction , 2001 .

[27]  Aleš Krainer,et al.  Integral control system of indoor environment in continuously occupied spaces , 2012 .

[28]  Francis Rubinstein,et al.  Preliminary Results from an Advanced Lighting Controls Testbed , 1998 .

[29]  Joseph Andrew Clarke,et al.  Simulation-assisted control in building energy management systems , 2002 .