참고문헌
- Moon, J.W., Yoon, S.H., Kim, S. Development of an artificial neural network model based thermal control logic for double skin envelopes in winter, Building and Environment; 61,149-59, 2013. https://doi.org/10.1016/j.buildenv.2012.12.010
- Moon, J.W., Chang, J.D., Kim, S. Artificial neural network for controlling the openings of double skin envelopes and cooling systems, International Conference on Sustainable Design and Construction; Texas (USA), 81-9, 2012.
- Moon JW, Kim SY. Artificial neural network for the control of the openings and cooling systems of the double skin envelope buildings, Advanced Materials Research; 610-613, 2859-65, 2013.
- Kim, Y.M., Lee, J.H., Kim, S.M., Kim, S. Effect of double skin envelopes on natural ventilation and heating in office buildings, Energy and Buildings; 43, 2118-2126, 2011. https://doi.org/10.1016/j.enbuild.2011.04.012
- Fallahi, A., Haghighat, F., Elsadi, H. Energy performance assessment of double-skin façade with thermal mass, Energy and Buildings; 4, 1499-1509, 2010.
- Kim, Y.M., Kim, S., Shin, S.W., Sohn, J.Y. Contribution of natural ventilation in a double skin envelope to heating load reduction in winte, Building and Environment; 44, 2236-2244, 2009. https://doi.org/10.1016/j.buildenv.2009.02.013
- Saelens, D., Roels, S., Hens, H. Strategies to improve the energy performance of multiple-skin facades, Building and Environment; 43, 638-650, 2008. https://doi.org/10.1016/j.buildenv.2006.06.024
- Gratia, E., Herde, A.F. Are energy consumption decreased with the addition of a double-ski, Energy and Buildings; 39, 605-619, 2007. https://doi.org/10.1016/j.enbuild.2006.10.002
- Lee E.S., Selkowitz, S., Bazjanac, S.V., Kholer, C. High-performance commercial building facades, LBNL Report-50502. Berkeley: Lawrence Berkeley National Laboratory, 2002.
- Moon, J.W., Lee, J.H., Chang, J.D. Sooyoung Kim. Preliminary performance tests on artificial neural network models for opening strategies of double skin envelopes in winter, Energy and Buildings; 75, 301-311, 2014. https://doi.org/10.1016/j.enbuild.2014.02.007
- MathWorks. MATLAB 14, vol. 26; 2010-3, http://www.mathworks.co m;2010
- Yang, J., Rivard, H., Zmeureanu, R. On-line building energy prediction using adaptive artificial neural networks, Energy and Buildings; 37, 1250-1259, 2005. https://doi.org/10.1016/j.enbuild.2005.02.005
- Datta, D., Tassou, S.A., Marriott, D. Application of Neural Networks for the Prediction of the Energy Consumption in a Supermarket, Clima 2000, Brussels (Belgium), 98-107, 2997.
- Moon, J.W., Jung, S.K., Kim, Y., Han, S.H. 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, 2011. https://doi.org/10.1016/j.applthermaleng.2011.04.006
- Moon, J.W., Chin, K.I., Kim, S. Optimum Application of Thermal Factors to Artificial Neural Network Models for Improvement of Control Performance in Double Skin-Enveloped Buildings, Energies; 6, 4223-4245, 2013. https://doi.org/10.3390/en6084223
- University of Wisconsin. TRNSYS16.1, http://sel.me.wisc.edu/trnsys/;2010.
- Moon, J.W. Performance of ANN-based predictive and adaptive thermal-control methods for disturbances in and around residential buildings, Building and Environment; 48:15-26, 2012. https://doi.org/10.1016/j.buildenv.2011.06.005
피인용 문헌
- Prediction Performance of an Artificial Neural Network Model for the Amount of Cooling Energy Consumption in Hotel Rooms vol.8, pp.8, 2015, https://doi.org/10.3390/en8088226