Optimum Application of Thermal Factors to Artificial Neural Network Models for Improvement of Control Performance in Double Skin-Enveloped Buildings

This study proposes an artificial neural network (ANN)-based thermal control method for buildings with double skin envelopes that has rational relationships between the ANN model input and output. The relationship between the indoor air temperature and surrounding environmental factors was investigated based on field measurement data from an actual building. The results imply that the indoor temperature was not significantly influenced by vertical solar irradiance, but by the outdoor and cavity temperature. Accordingly, a new ANN model developed in this study excluded solar irradiance as an input variable for predicting the future indoor temperature. The structure and learning method of this new ANN model was optimized, followed by the performance tests of a variety of internal and external envelope opening strategies for the heating and cooling seasons. The performance tests revealed that the optimized ANN-based logic yielded better temperature conditions than the non-ANN based logic. This ANN-based logic increased overall comfortable periods and decreased the frequency of overshoots and undershoots out of the thermal comfort range. The ANN model proved that it has the potential to be successfully applied in the temperature control logic for double skin-enveloped buildings. The ANN model, which was proposed in this study, effectively predicted future indoor temperatures for the diverse opening strategies. The ANN-based logic optimally determined the operation of heating and cooling systems as well as opening conditions for the double skin envelopes.

[1]  Jin Woo Moon,et al.  Artificial Neural Network for the Control of the Openings and Cooling Systems of the Double Skin Envelope Buildings , 2012 .

[2]  André De Herde,et al.  Natural cooling strategies efficiency in an office building with a double-skin facade , 2004 .

[3]  Soteris A. Kalogirou,et al.  Artificial neural networks for the prediction of the energy consumption of a passive solar building , 2000 .

[4]  Sooyoung Kim,et al.  Application of Artificial Neural Network for Optimum Controls of Windows and Heating Systems of Double-Skinned Buildings , 2012 .

[5]  Antonio Messineo,et al.  On the Evaluation of Solar Greenhouse Efficiency in Building Simulation during the Heating Period , 2012 .

[6]  Sooyoung Kim,et al.  Effects of double skin envelopes on natural ventilation and heating loads in office buildings , 2011 .

[7]  Giorgio Baldinelli,et al.  Double skin facades for warm climate regions : Analysis of a solution with an integrated movable shading system , 2009 .

[8]  Tullie Circle,et al.  AMERICAN SOCIETY OF HEATING, REFRIGERATING AND AIR-CONDITIONING , 2013 .

[9]  S. Kim,et al.  Artificial neural network for controlling the openings of double skin envelopes and cooling systems , 2012 .

[10]  Yu Min Kim,et al.  Contribution of natural ventilation in a double skin envelope to heating load reduction in winter , 2009 .

[11]  Jin Woo Moon,et al.  Development of an artificial neural network model based thermal control logic for double skin envelopes in winter , 2013 .