Journal of the Korean Solar Energy Society (한국태양에너지학회 논문집)

The Korean Solar Energy Society (한국태양에너지학회)
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A Proposal of an Interpolation Method of Missing Wind Velocity Data in Writing a Typical Weather Data

표준기상데이터 작성 시 누락된 풍속 데이터의 보간 방법 제안

  • Park, So-Woo (Department of Civil and Environmental System Engineering, Sungkyunkwan University) ;
  • Kim, Joo-wook (School of Civil, Architectural Engineering, and Landscape Architecture, Sungkyunkwan University) ;
  • Song, Doo-sam (School of Civil, Architectural Engineering, and Landscape Architecture, Sungkyunkwan University)
  • 박소우 (성균관대학교 건설환경시스템공학과) ;
  • 김주욱 (성균관대학교 건설환경공학부) ;
  • 송두삼 (성균관대학교 건설환경공학부)
  • Received : 2017.11.10
  • Accepted : 2017.12.12
  • Published : 2017.12.30
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Abstract

The meteorological data of 1 hour interval are required to write a typical weather data for building energy simulation. However, many meterological data are missing and the interpolation method to recover the missing data is required. Especially, lots of meterological data are replicated by linear interpolation method because the changes are not significant. While, the wind velocity fluctuates with the time or locations, so linear interpolation method is not appropriate in interpolation of the wind velocity data. In this study, three interpolation methods, using surrounding wind velocity data, Inverse Distance Weighting (IDW), Revised Inverse Distance Weighting (IDW-r), were analyzed considering the characteristics of wind velocity. The Revised Inverse Distance Weighting method, proposed in this study, showed the highest reliability in restoration of the wind velocity data among the analyzed methods.

Keywords

References

  1. Korea Energy Economics Institute, Yearbook of energy statistics, 2016.
  2. Shim, J. S. and Song, D. S., Applicability of the Solar Irradiation Model in Preparation of Typical Weather Data Considering Domestic Climate Conditions, Journal of the Korea Air-Conditioning and Refrigeration Engineering, Vol. 28, No.12, pp. 467-476, 2016. https://doi.org/10.6110/KJACR.2016.28.12.467
  3. Park, S. H., Typical Weather Data Establishment and Climate Zone Development for Building Energy Assessment -Focused on the Major Cities in South Korea-, Doctorate thesis, pp. 23-51, 2013.
  4. Baltazar, J. C. and Claridge, D. E., Study of Cubic Splines and Fourier Series as Interpolation Techniques for Filling in Short Periods of Missing Building Energy Use and Weather Data, Journal of Solar Energy Engineering, Vol. 128, No. 2, pp. 226-230, 2005.
  5. Kim, G. H., Youn, J. H. and Kim, B. S., Producing Wind Speed Maps Using Gangwon Weather Data, Journal of the Korean Society for Geo-spatial Information Science, Vol. 18, No. 1, pp. 31-39, 2010.
  6. Ibrahim, M. Z., Yong, K. H., Ismail, M., Albani, A. and Muzathik, A. M., Wind Characteristics and Gis-based Spatial Wind Mapping Study in Malaysia, Journal of Sustainability Science and Management, Vol. 9, No. 2, pp. 1-20, 2014. https://doi.org/10.1007/s11625-013-0228-2
  7. Ali, S. M., Mahdi, A. S. and Shaban, A. H., Wind Speed Estimation for Iraq using several Spatial Interpolation Methods, British Journal of Science, Vol. 7, No. 2, pp. 48-55, 2012.
  8. International Standard ISO 15927-4, Hygrothermal Performance of Buildings-Calculation and Presentation of Climatic Data - Part4: Hourly Data for Assessing the Annual Energy Use for Heating and Cooling, ISO, 2005.
  9. You, H. C., Noh, K. H., Kang, H. G., and Shin, I. H., Comparison and Analysis of Typical Metrological Data by in Korea, Autumn Annual Conference of the Korea Solar Energy Society, pp. 361-366, 2009.
  10. The Wind Engineering Institute of Korea, Wind Engineering for Engineers, Kimoondang, Seoul, pp. 236-237, 2010.
  11. Palarya, S., Introduction to Micrometeorology, 2nd ed, Sigmapress, Seoul, pp. 154-157, 2003.
  12. Ye, W., Spatial Variation and Interpolation of Wind Speed Statistics and Its Implication in Design Wind Load, Doctorate thesis, pp. 9-28, 2013.
  13. Luo, W., Taylor, M. C. and Parker, S. R., A Comparison of Spatial Interpolation Methods to Estimate Continuous Wind Speed Surfaces Using Irregularly Distributed Data from England and Wales, International Journal of Climatology, Vol. 28, No. 7, pp. 947-959, 2008. https://doi.org/10.1002/joc.1583
  14. Sluiter, R., Interpolation Methods for Climate Data -Literature Review, Koninklijk Netherlands Meteorological Institute, 2009.
  15. Han, M. S., Kim, C. S., Kin, H. S., and Kim, H. R., A Study on the Revised Methods of Missing Rainfall Data for Real-time Forecasting Systems, Journal of the Korea Water Resources Association, Vol. 42, No. 2, pp. 131-139, 2009. https://doi.org/10.3741/JKWRA.2009.42.2.131
  16. Jeong, S. H., Kim, B. J., and Ha, Y. C., Revision of Basic Wind Speed Map of KBC-2009, Journal of the Architectural Institute of Korea, Vol. 30, No. 5, pp. 37-47, 2014.
  17. Choi, S. H. and Seo, E. S., Estimating Method of Surface Roughness Using Geographic Information, Journal of the Korean Association of Geographic Information Studies Vol. 18, No.3, pp. 1-10, 2015. https://doi.org/10.11108/KAGIS.2015.18.3.001
  18. Park, S. W. and Song. D. S., Interpolation Method and Its Reliability Evaluation of Wind Speed Data in Writing Typical Weather Data, Spring Annual Conference of the Korea Air-Conditioning and Refrigeration Engineering, pp. 759-762, 2017.

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