[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.12813/kieae.2014.14.3.097

Development of Integrated Control Methods for the Heating Device and Surface Openings based on the Performance Tests of the Rule-Based and Artificial-Neural-Network-Based Control Logics

Moon, Jin Woo (Dept. of Building and Plant Engineering, Hanbat National University)

Publication Information

KIEAE Journal / v.14, no.3, 2014 , pp. 97-103 More about this Journal

Abstract

This study aimed at developing integrated logic for controlling heating device and openings of the double skin facade buildings. Two major logics were developed-rule-based control logic and artificial neural network based control logic. The rule based logic represented the widely applied conventional method while the artificial neural network based logic meant the optimal method. Applying the optimal method, the predictive and adaptive controls were feasible for supplying the advanced thermal indoor environment. Comparative performance tests were conducted using the numerical computer simulation tools such as MATLAB (Matrix Laboratory) and TRNSYS (Transient Systems Simulation). Analysis on the test results in the test module revealed that the artificial neural network-based control logics provided more comfortable and stable temperature conditions based on the optimal control of the heating device and opening conditions of the double skin facades. However, the amount of heat supply to the indoor space by the optimal method was increased for the better thermal conditioning. The number of on/off moments of the heating device, on the other hand, was significantly reduced. Therefore, the optimal logic is expected to beneficial to create more comfortable thermal environment and to potentially prevent system degradation.

Keywords

Rule-based controls; Optimal controls; Artificial neural network; Double skin facades; Thermal environment;

Citations & Related Records

Reference

1	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. DOI ScienceOn
2	University of Wisconsin. TRNSYS16.1, http://sel.me.wisc.edu/trnsys/;2010.
3	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. DOI ScienceOn
4	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.
5	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. DOI ScienceOn
6	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. DOI ScienceOn
7	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.
8	Saelens, D., Roels, S., Hens, H. Strategies to improve the energy performance of multiple-skin facades, Building and Environment; 43, 638-650, 2008. DOI ScienceOn
9	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. DOI
10	Fallahi, A., Haghighat, F., Elsadi, H. Energy performance assessment of double-skin façade with thermal mass, Energy and Buildings; 4, 1499-1509, 2010.
11	Gratia, E., Herde, A.F. Are energy consumption decreased with the addition of a double-ski, Energy and Buildings; 39, 605-619, 2007. DOI ScienceOn
12	Lee E.S., Selkowitz, S., Bazjanac, S.V., Kholer, C. High-performance commercial building facades, LBNL Report-50502. Berkeley: Lawrence Berkeley National Laboratory, 2002.
13	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. DOI ScienceOn
14	MathWorks. MATLAB 14, vol. 26; 2010-3, http://www.mathworks.co m;2010
15	Yang, J., Rivard, H., Zmeureanu, R. On-line building energy prediction using adaptive artificial neural networks, Energy and Buildings; 37, 1250-1259, 2005. DOI ScienceOn
16	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.
17	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. DOI ScienceOn