A Stable Energy Saving Adaptive Control Scheme for Building Heating and Cooling Systems

Sumera I. Chaudhry, Manohar Das

Department of Electrical and Computer Engineering, Oakland University, Rochester, USA.

DOI: 10.4236/jpee.2014.25002                

Abstract

This paper presents a stable, nonlinear, adaptive control scheme for building heating and cooling systems. The proposed controller utilizes the principle of adaptive one step ahead control and aims at reducing the energy consumed for heating or cooling a building. The design steps are discussed in details and a proof of global stability is also provided. Also, the performance of the proposed controller is demonstrated on a simulated building thermal model.

Keywords

Building HVAC Control, Adaptive Control, Energy Saving Control

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Jimenez, M.J., Madsen, H. and Andersen, K.K. (2008) Identification of the Main Thermal Characteristics of Building Components Using Matlab. Building and Environment, 43, 170-180. http://dx.doi.org/10.1016/j.buildenv.2006.10.030
[2]	Paris, B., Eynard, J., Grieu, S., Talbert, T. and Polit, M. (2010) Heating Control Schemes for Energy Management in Buildings. Energy and Buildings, 42, 1908-1917. http://dx.doi.org/10.1016/j.enbuild.2010.05.027
[3]	Dounis, A.I. and Caraiscos, C. (2009) Advanced Control Systems Engineering for Energy and Comfort Management in a Building Environment—A Review. Renewable and Sustainable Energy Reviews, 13, 1246-1261. http://dx.doi.org/10.1016/j.rser.2008.09.015
[4]	Moon, J.W., Jung, S.K., Kim, Y. and Han, S.H. (2011) Comparative Study of Artificial Intelligence Based Building Thermal Control Methods—Application of Fuzzy, Adaptive Neuro-Fuzzy Inference System, and Artificial Neural Network. Applied Thermal Engineering, 31, 2422-2429. http://dx.doi.org/10.1016/j.applthermaleng.2011.04.006
[5]	Balan, R., Cooper, J., Chao, K.M., Stan, S. and Donca, R. (2011) Parameter Identification and Model Based Predictive Control of Temperature inside a House. Energy and Building, 43, 748-758. http://dx.doi.org/10.1016/j.enbuild.2010.10.023
[6]	Ma, Y., Borrelli, F., Hencey, B., Coffey, B., Bengea, S. and Haves, P. (2010) Model Predictive Control for the Operation of Building Cooling Systems. IEEE Transactions on Control Systems Technology, 20, 796-803.
[7]	Calvino, F., Gennusa, M.L., Morale, M., Rizzo, G. and Scaccianoce, G. (2010) Comparing Different Control Strategies for Indoor Thermal Comfort Aimed at the Evaluation of the Energy Cost of Quality of Building. Applied Thermal Engineering, 30, 2386-2395. http://dx.doi.org/10.1016/j.applthermaleng.2010.06.008
[8]	Orosa, J.A. (2011) A New Modeling Methodology to Control HVAC Systems. Expert Systems with Applications, 38, 4505-4513. http://dx.doi.org/10.1016/j.eswa.2010.09.124
[9]	Goodwin, G.C. and Sin, K.S. (1984) Adaptive Filtering Prediction and Control. Prentice-Hall, Englewood Cliffs.
[10]	IBPT (2012) International Building Physics Toolbox in Simulink. http://www.ibpt.org/
[11]	Antsaklis, P.J. and Michel, N.A. (2007) A Linear Systems Primer. Birkhauser (Springer), New York.
[12]	Dochain, D. and Bastin, G. (1984) Adaptive Identification and Control Algorithms for Nonlinear Bacterial Growth Systems. Automatica, 20, 621-634. http://dx.doi.org/10.1016/0005-1098(84)90012-8
[13]	Goodwin, G.C., McInnis, B. and Long, R.S. (1982) Adaptive Control Algorithms for Waste Water Treatment and pH Neutralization. Optimal control Applications and Methods, 3, 443-459.
[14]	Fanger, P.O. (1972) Thermal Comfort: Analysis and Applications in Environmental Engineering. McGraw-Hill, New York.