산업공학 (IE interfaces)

  • 제24권4호
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  • Pages.373-378
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  • 2011
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  • 1225-0996(pISSN)
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  • 2234-6465(eISSN)
대한산업공학회 (Korean Institute of Industrial Engineers)
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지역 난방을 위한 열 수요예측

Heat Demand Forecasting for Local District Heating

  • 송기범 (성균관대학교 시스템경영공학과) ;
  • 박진수 (성균관대학교 시스템경영공학과) ;
  • 김윤배 (성균관대학교 시스템경영공학과) ;
  • 정철우 (성균관대학교 시스템경영공학과) ;
  • 박찬민 (성균관대학교 시스템경영공학과)
  • Song, Ki-Burm (Department of Systems Management Engineering, Sungkyunkwan University) ;
  • Park, Jin-Soo (Department of Systems Management Engineering, Sungkyunkwan University) ;
  • Kim, Yun-Bae (Department of Systems Management Engineering, Sungkyunkwan University) ;
  • Jung, Chul-Woo (Department of Systems Management Engineering, Sungkyunkwan University) ;
  • Park, Chan-Min (Department of Systems Management Engineering, Sungkyunkwan University)
  • 투고 : 2011.08.30
  • 심사 : 2011.11.08
  • 발행 : 2011.12.01
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초록

High level of accuracy in forecasting heat demand of each district is required for operating and managing the district heating efficiently. Heat demand has a close connection with the demands of the previous days and the temperature, general demand forecasting methods may be used forecast. However, there are some exceptional situations to apply general methods such as the exceptional low demand in weekends or vacation period. We introduce a new method to forecast the heat demand to overcome these situations, using the linearities between the demand and some other factors. Our method uses the temperature and the past 7 days' demands as the factors which determine the future demand. The model consists of daily and hourly models which are multiple linear regression models. Appling these two models to historical data, we confirmed that our method can forecast the heat demand correctly with reasonable errors.

키워드

참고문헌

  1. Baek, J.-K. and Han, J.-H. (2011), Forecasting of Heat Demand in Winter Using Linear Regression Models for Korea District Heating Corporation, Journal of the Korea Academia-Industrial Cooperation Society, 12(3), 1488-1494. https://doi.org/10.5762/KAIS.2011.12.3.1488
  2. Chramcov, B. (2010), Heat demand forecasting for concrete district heating system, International Journal of Mathematical Models and Methods in Applied Sciences, 4(4), 231-239.
  3. Dotzauer, E. (2002), Simple model for prediction of loads in district-heating systems, Journal of Applied Energy, 73(3-4), 277-284. https://doi.org/10.1016/S0306-2619(02)00078-8
  4. Hippert, H. S., Pedreira, C. E., and Souza, R. C. (2001), Neural networks for short-term load forecasting : a review and evaluation, Journal of IEEE Transactions on Power Systems, 16(1), 45-55
  5. Kvarnsöm, J., Liljedahl, J., and Dotzauer, E. (2006), Forward temperatures and production planning in district heating systems, 10th International Symposium on District Heating and Cooling, 1-6.
  6. Park, T. C., Kim, U. S., Kim, L. H., Kim, W. H., and Yeo, Y. K. (2009), Optimization of district heating systems based on the demand forecast in the capital region, Korean Journal of Chemical Engineering, 26(6), 1484-1496. https://doi.org/10.1007/s11814-009-0282-8
  7. Torkar, J., Goricanec, D., and Krope, J. (2005), Economical heat roduction and distribution, Proceedings of 3rd IASME/WSEAS International Conference on Heat Transfer, 445.
  8. Tsakoumis, A. C., Fessas, P., Mladenov, V. M., and Mastorakis, N. E. (2003), Application of Neural Networks for Short Term Electric Load Prediction, WSEAS Transactions on Systems, 2(3), 513-516.
  9. Tsekouras, G. J., Kanellos, F. D., Elias, CH. N., Kontargyri, V. T., Tsirekis, C. D., Karanasiou, I. S., Salis, A. D., Contaxis, P. A., Gialketsi, A. A., and Mastorakis, N. E. (2009), Short Term Load Forecasting in Greek Intercontinental Power System using ANNs : a Study for Input Variables, Proceedings of the 10th WSEAS International Conference on Neural Networks, 193.

피인용 문헌

  1. Short-Term Forecasting of City Gas Daily Demand vol.39, pp.4, 2013, https://doi.org/10.7232/JKIIE.2013.39.4.247