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Study on Prediction of Solar Insolation and Heating Load

일사량 및 난방부하 예측에 관한 연구

  • Yoo, Seong Yeon (Dept. of Mechanical Design Engineering, Chungnam Nat'l Univ.) ;
  • Kim, Tae Ho (Dept. of Mechanical Design Engineering, Chungnam Nat'l Univ.) ;
  • Han, Kyu Hyun (Dept. of Mechanical Design Engineering, Chungnam Nat'l Univ.) ;
  • Kim, Myung Ho (Dept. of Mechanical Design Engineering, Chungnam Nat'l Univ.)
  • 유성연 (충남대학교 기계설계공학과) ;
  • 김태호 (충남대학교 기계설계공학과) ;
  • 한규현 (충남대학교 기계설계공학과) ;
  • 김명호 (충남대학교 기계설계공학과)
  • Received : 2013.05.09
  • Accepted : 2013.09.29
  • Published : 2013.12.01

Abstract

In this study, a method for predicting heating loads using building characteristic coefficients is proposed for heating system control, and a method for predicting hourly temperature and solar insolation, which mainly affect building heating loads, is also proposed. The temperature and solar insolation are predicted by using a fuzzy theory from forecast information at the meteorological agency, and the building characteristic coefficients for the prediction of heating loads are derived from EnergyPlus. The simulated heating loads of the present study show good agreement with those of EnergyPlus. and the variations of the predicted heating loads using the predicted temperature and solar insolation are similar to those using the actual weather data.

본 연구에서는 난방설비 제어에 필요한 난방부하를 건물 특성계수를 사용하여 예측하는 방법을 제안하였고, 난방부하에 주된 영향을 미치는 시간별 온도와 일사량을 예측하는 방법을 제안하였다. 온도와 일사량은 기상청에서 예보되는 정보로부터 퍼지이론을 이용하여 예측하였고, 난방부하 예측을 위한 건물 특성계수는 EnergyPlus로부터 도출하였다. 본 연구에서 제안된 방법으로 얻어진 난방부하는 EnergyPlus의 결과와 잘 일치하였으며, 예측된 온도와 일사를 이용하여 예측한 난방부하의 변화 양상은 실측 기상데이터를 사용한 결과와 유사하였다.

Keywords

References

  1. Korea Energy Management Corporation, 2008, "Energy Saving Statistics".
  2. Lee, K. H. and Braun, J. E., 2008, "Performance Evaluation of a Dynamic Inverse Model with EnergyPlus Model Simulation for Building Cooling Loads," Trans. of SAREK, Vol. 20, No. 3, pp. 205-212.
  3. Braun, J. E. and Chaturvedi, N., 2002, "An Inverse Gray-Box Model for Transient Building Load Prediction," HVAC&R Research, Vol. 8, No. 1, pp. 73-99. https://doi.org/10.1080/10789669.2002.10391290
  4. Lee, K. H., Yang, S. K. and Han, S. H., 2010, "Reducing Peak Cooling Demand Using Building Precooling and Modified Linear Rise of Indoor Space Temperature," Trans. of SAREK, Vol. 22, No. 2, pp. 86-96.
  5. Zhou, Q., Wang, S., Xu, X. and Xiao, F., 2008, "A Grey-box Model of Next-day Building Thermal Load Prediction for Energy-efficient Control," Int. J. Energy Res., Vol. 32, pp. 1418-1431. https://doi.org/10.1002/er.1458
  6. Yoo, S. Y. and Han, K. H., 2010, "A Study on Prediction of Hourly Cooling Load Using Building Area," Trans. of SAREK, Vol. 22, No. 11, pp. 798-804.
  7. Yoo, S. Y., Kim, T. H., Han, K. H., Yoon, H. I., Kang, H. C. and Kim, K. H., 2012, "Prediction of Heating Load for Optimum Heat Supply in Apartment Building," Trans. Korean Soc. Mech. Eng. B, Vol. 32, No. 8, pp. 803-809. https://doi.org/10.3795/KSME-B.2012.36.8.803
  8. Seem, J. E., Klein, S. A., Beckman, W. A. and Mitchell, J. W., 1989, "Transfer Functions for Efficient Calculations of Multi Dimensional Heat Transfer," J. of Heat Transfer-Transaction of the ASME, Vol. 111, No. 1, pp. 5-12. https://doi.org/10.1115/1.3250659
  9. Moon, H. J., 2009, "Building Energy Simulation using EnergyPlus and BIM's Application," J. of the KARSE, Vol. 26, No. 9, pp. 28-37.