Artificial Neural Network for the Control of the Openings and Cooling Systems of the Double Skin Envelope Buildings

Abstract:

This study aimed at developing an artificial neural network (ANN)-based temperature control method for the double skin envelope buildings. For this, control logic for opening conditions of the inner and outer surfaces’ openings as well as for cooling system’s operation was developed based on the predictive and adaptive ANN model. The parametrical optimization process for the structure and learning methods of the ANN model was conducted in terms of the number of hidden layers, the number of neurons in the hidden layers, learning rate, and moment. Then, the performance of this optimized model was tested using the similarity analysis between the predicted values from the ANN model and the measured values from the actual double skin envelope building. Analysis revealed that the developed ANN model proved its prediction accuracy and adaptability in terms of stable Root Mean Square (RMS) and Mean Square Error (MSE) values. Based on this finding, it can be concluded that the developed ANN model showed potentials to be successfully applied to the temperature controls for the double skin envelope buildings.

• Periodical: Advanced Materials Research (Volumes 610-613)
• Pages: 2859-2865
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.610-613.2859
• Online since: December 2012
• Authors: Jin Woo Moon, Soo Young Kim
• Keywords: Artificial Neural Network (ANN), Double Skin Envelope, Predictive and Adaptive Controls, Thermal Conditioning

References

[1] S.H Yoon, C.S. Park, Control Levels of a Double-Skin Facade, Journal of Architectural Institute of Korea Planning & Design 26 (2010) 307-326.

[2] E. Gratia, A.D. Herde, Natural cooling strategies efficiency in an office building with a double-skin façade, Energy and Buildings 36 (2004) 1139-1152.

DOI: 10.1016/j.enbuild.2004.05.004

[3] J.W. Moon, J.J. Kim, ANN-based thermal control models for residential buildings, Building and Environment 45 (2010) 1612-1625.

DOI: 10.1016/j.buildenv.2010.01.009

[4] J.W. Moon, Performance of ANN-based predictive and adaptive thermal-control methods for disturbances in and around residential buildings, Building and Environment 48 (2011) 15-26.

DOI: 10.1016/j.buildenv.2011.06.005

[5] J.Y. Lee, M.S., Yeo, K.W., Kim, Predictive Control of the Radiant Floor Heating System in Apartment Buildings, Journal of Asian Architecture and Building Engineering 1 (2002) 105-112.

DOI: 10.3130/jaabe.1.105

[6] MathWorks, MATLAB 14, Information on http://www.mathworks.com.

[7] Datta D, Tassou SA, Marriott D, Application of neural networks for the prediction of the energy consumption in a supermarket. In: Proc, of CLIMA 2000 Conf, Brussels, Belgium (1997) 98–107.

[8] I.H. Yang, M.S. Yeo, K.W. Kim, Application of artificial neural network to predict the optimal time for heating system in building, Energy Conversion and Management 44 (2003) 2791-2809.

DOI: 10.1016/s0196-8904(03)00044-x

[9] S.A. Kalogirou, M. Bojic, Artificial neural networks for the prediction of the energy consumption of a passive solar building, Energy 25 (2000) 479–491.

DOI: 10.1016/s0360-5442(99)00086-9

[10] Y.M. Kim, S.Y. Kim, S.W. Shin, J.Y. Sohn, Contribution of natural ventilation in a double skin envelope to heating load reduction in winter, Building and Environment 44 (2009) 2236-2244.

DOI: 10.1016/j.buildenv.2009.02.013

[11] Y.M. Kim, J.H. Lee, S.M. Kim, S. Kim, Effects of double skin envelopes on natural ventilation and heating loads in office buildins, Energy and Buildings 43(2011) 2118-2126.

DOI: 10.1016/j.enbuild.2011.04.012