KIEAE Journal

Korea Institute of Ecological Architecture and Environment (한국생태환경건축학회)
권호 표지
DOI QR코드

DOI QR Code

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

난방시스템 및 개구부의 통합제어를 위한 규칙기반제어법 및 인공신경망기반제어법의 성능비교

  • Moon, Jin Woo (Dept. of Building and Plant Engineering, Hanbat National University)
  • Received : 2014.05.06
  • Accepted : 2014.05.19
  • Published : 2014.06.30
Download PDF
⟨ Previous Next ⟩

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

References

  1. 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
  2. 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.
  3. 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.
  4. 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
  5. Fallahi, A., Haghighat, F., Elsadi, H. Energy performance assessment of double-skin façade with thermal mass, Energy and Buildings; 4, 1499-1509, 2010.
  6. 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
  7. 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
  8. 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
  9. Lee E.S., Selkowitz, S., Bazjanac, S.V., Kholer, C. High-performance commercial building facades, LBNL Report-50502. Berkeley: Lawrence Berkeley National Laboratory, 2002.
  10. 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
  11. MathWorks. MATLAB 14, vol. 26; 2010-3, http://www.mathworks.co m;2010
  12. 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
  13. 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.
  14. 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
  15. 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
  16. University of Wisconsin. TRNSYS16.1, http://sel.me.wisc.edu/trnsys/;2010.
  17. 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

Cited by

  1. 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