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A Multi-objective Optimization Method for Energy System Design Considering Initial Cost and Primary Energy Consumption

초기투자비와 1차 에너지소비량을 고려한 에너지시스템의 다중최적 설계 방법론

  • Kong, Dong-Seok (Department of Architectural Engineering, University of Seoul) ;
  • Jang, Yong-Sung (GS E&C Building Science Research Team) ;
  • Huh, Jung-Ho (Department of Architectural Engineering, University of Seoul)
  • 공동석 (서울시립대학교 건축공학과) ;
  • 장용성 (GS건설기술연구소) ;
  • 허정호 (서울시립대학교 건축공학과)
  • Received : 2014.03.25
  • Accepted : 2014.05.16
  • Published : 2014.08.10

Abstract

This paper proposed a multi-objective optimization method for building energy system design using primary energy consumption and initial cost. The designing of building energy systems is a complex task, because life cycle cost and efficiency of building are determined by decisions of engineer during the early stage of design. Therefore, methods such as pareto analysis that can generate various alternatives for decision making are necessary. In this study, the optimization is performed using the NSGAII and case study was carried out for feasibility of the proposed method. As a result, alternative solutions can be obtained for the optimal building energy system design.

Keywords

References

  1. Jacobs, P. and Henderson, H., 2002, State of the art review of whole building, building envelope, and HVAC component and system simulation and design tools, Final report ARTI-21CR/30010-01, Air-Conditioning and Refrigeration Technology Institute, Arlington, VA.
  2. Hafez, O. and Bhattacharya, K., 2012, Optimal planning and design of a renewable energy based supply system for microgrids, Renewable Energy, Vol. 45, pp. 7-15. https://doi.org/10.1016/j.renene.2012.01.087
  3. Kayo, G. and Ooka, R., 2010, Building energy system optimizations with utilization of waste heat from cogenerations by means of genetic algorithm, Energy and Buildings, Vol. 42, No. 7, pp. 985-991. https://doi.org/10.1016/j.enbuild.2010.01.010
  4. Ooka, R. and Komamura, K., 2009, Optimal design method for building energy systems using genetic algorithms, Building and Environment, Vol. 44, No. 7 pp. 1538-1544. https://doi.org/10.1016/j.buildenv.2008.07.006
  5. Kayo, G. and Ooka, R., 2009, Optimal design method for distributed energy system using genetic algorithm: Examining the influence of GA parameters on the accuracy of calculation results estimated by the optimal design method and confirming the applicability of designing distributed energy system, Journal of Environmental Engineering-Transactions of Architectural Institute of Japan, Vol. 74, No. 641, pp. 869-876.
  6. Kayo, G. and Ooka, R., 2010, Multi-objective genetic algorithm optimized for energy consumption and cost in building energy system design, Journal of Environmental Engineering-Transactions of Architectural Institute of Japan, Vol. 75, No. 654, pp. 735-740.
  7. Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T., 2002, A fast and elitist multi-objective genetic algorithm : NSGA-II, IEEE Transactions on Evolutionary Computation, Vol. 6, No. 2, pp. 182-197. https://doi.org/10.1109/4235.996017
  8. Burdick, A., 2012, Strategy guideline : HVAC equipment sizing, Building Technologies Program, US Department of Energy, Energy Efficiency and Renewable Energy.
  9. Perez-Lombard, L., Ortiz, J., Coronel, J. F., and Maestre, I. R., 2011, A review of HVAC systems requirements in building energy regulations, Energy and Buildings, Vol. 43, No. 2-3, pp. 255-268. https://doi.org/10.1016/j.enbuild.2010.10.025
  10. ASHRAE fundamentals handbook chapter 28 non-residential cooling and heating load calculations, 1997, American Society of Heating, Refrigerating and Airconditioning Engineers.
  11. Elkhuizen, P. A., Peitsman, H. C., and Wienk, W. J., 2003, A new design guideline for the heating and cooling curve in AHU of HVAC systems, Building Services Engineering Research and Technology, Vol. 24, No. 1, pp. 191-202. https://doi.org/10.1191/0143624403bt071oa
  12. Hamdy, M., Hasan, A., and Siren, K., 2013, A multi-stage optimization method for cost-optimal and nearly-zero-energy building solutions in line with the EPBD-recast 2010, Energy and Buildings, Vol. 56, pp. 189-203. https://doi.org/10.1016/j.enbuild.2012.08.023
  13. Attia, S., Hamdy, M., O'Brien, W., and Carlucci, S., 2013, Assessing gaps and needs for integrating building performance optimization tools in net zero energy buildings design, Energy and Buildings, Vol. 60, pp. 110-124. https://doi.org/10.1016/j.enbuild.2013.01.016
  14. EnergyPlus engineering reference, 2013, US Department of Energy.
  15. Korea Price Information Corp. website, http://www.kpi.or.kr.
  16. Djunaedy, E., van den Wymelenberg, K., Acker, B., and Thimmana, H., 2011, Oversizing of HVAC system : Signatures and penalties, Energy and Buildings, Vol. 43, No. 2-3, pp. 468-475. https://doi.org/10.1016/j.enbuild.2010.10.011
  17. Kong, D. S., Jang, Y. S., Ann, M. H., and Huh, J. H., 2012, A study on the methodology of design optimization in cooling system, Proceedings of the SAREK 2012 Summer Annual Conference, No. 6, pp. 632-635.

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