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서울지역 표준기상데이터 개선을 위한 대표적인 산출방법의 검토 및 평가

The Review & Evaluation of Calculation Methods for Improving of Typical Weather Data for Seoul

  • 투고 : 2012.04.05
  • 발행 : 2012.06.25

초록

Typical Weather data is an essential factor for predicting building performance. As the thermal simulation programmes become more complicated, more accurate and specific weather data is needed. In this paper, various types of typical weather data sets including the Sandia TMYs, NREL TMY2, Pissimanis TMYs, IWEC, ISO TRY and CIBSE TRY were reviewed. Typical Weather data have been generated, using ten different methods(namely maximum, minimum and mean air temperature and relative humidity, maximum and mean wind speed and daily global radiation, daily direct radiation) from hourly meteorological data measured in Seoul, covering the period 1991 to 2010. Results of the comparison show that the most appropriate method for generating weather data depends on the selection process and climatic elements rather than weighting factor. In other words, the weighing factor is not the only element that determines a suitable weather data. Therefore, The weighting factor and calculation process, meteorological element are considered to reflect geographical characteristics of local and applied climatic elements.

키워드

과제정보

연구 과제 주관 기관 : 한국연구재단

참고문헌

  1. 녹색성장위원회, 온실가스 감축목표, http://www.greengrowth.go.kr
  2. Hall I.J., Prairie R. R., Anderson H. E. and Boes E. C., Generation of Typical Meteorological Years for 26 SOLMET stations, Sandia laboratories Report SAND 78-1601, Sandia Laboratories, Albuquerque, NM, 1978
  3. Willian Marion and Ken Urban, User's Manual for TMY2s, NREL, 1995
  4. Pissimanis D., Karras G., Notaridou V. and Gavra K., The generation of a 'Typical meteorological Year' for the city of Athens, Solar Energy, 40, 1988
  5. Qingshan Xu, Haixiang Zang, Comments on :Generation of typical meteorological year for different climates of China", Energy, 35, 2011
  6. Yingni Jiang, Generation of typical meteorological year for different climates of China, Energy, 35, 2010
  7. Naseem M. Sawaqed, Yousef H. Zurigat and Hilal Al-Hinai, A step-by-step application of Sandia method in developing typical meteorological years for different locations in Oman, Int. J. Energy Res, 29, 2005
  8. A. Argiroiu, S. Lykoudis, S. Kontoyiannidis, C. A. Balaras, D. Asimakopoulos, M. Petrakis and P. Kassomenos, Comparison of Methodologies for TMY Generation using 20 years data for Athens, Greece, Solar Energy, 66(1), 1999
  9. Liu Yang, Joseph C. Lam, Jiaping Liu, Analysis of typical meteorological years in different climates of China, Energy Coversion and Management, 48, 2007
  10. CIBSE, CIBSE Guide J: Weather, solar and illuminance data, London: The Chartered Institution of Building Services Engineers, 2002
  11. G.J. Levermore & J.B. Parkinson, Analyses and algorithms for new Test Reference Years and Desing Summer Years for the UK, Building Serv. Eng. Res. Technol, 27(4), 2006
  12. Selection of typical weather data(test reference years) for Subang, Malaysia, Building and Environment, 2007
  13. International Standard ISO 15972-4, Hygrothermal performance of buildings-Calculation and presentation of climatic data-Part 4:Hourly data for assessing the annual energy use for heating and cooling, 2005
  14. ASHRAE, Inernational Weather for Energy Calculations(IWEC Weather Files) User's Manual, Version 1.1, 2002
  15. 김두천, 서울지방의 표준기상데이터에 관한 연구, 대한설비공학회논문집, 14(2), 1985
  16. 김두천, 서진석, 한국 주요도시의 HASP용 표준기상데이터의 개발, 22(4), 공기조화 냉동공학회 논문집, 1993
  17. 유호천, 박소희, 김경률, 서울지역의 표준기상데이터 산출방법론 비교, 28(2), 한국태양에너지학회, 2008
  18. Filkenstein J. M. and Schafer R. E., Improved goodness to fit tests, Biometrica, 58, 1971
  19. Merter Uner and Arif Ileri, Typical weather data of main Turkish cities for energy applications, International Journal of Energy Research, 24, 2000