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Temperature prediction at critical points in district heating systems

Author

Listed:
  • Pinson, P.
  • Nielsen, T.S.
  • Nielsen, H.Aa.
  • Poulsen, N.K.
  • Madsen, H.

Abstract

Current methodologies for the optimal operation of district heating systems use model predictive control. Accurate forecasting of the water temperature at critical points is crucial for meeting constraints related to consumers while minimizing the production costs for the heat supplier. A new forecasting methodology based on conditional finite impulse response (cFIR) models is introduced, for which model coefficients are replaced by coefficient functions of the water flux at the supply point and of the time of day, allowing for nonlinear variations of the time delays. Appropriate estimation methods for both are described. Results are given for the test case of the Roskilde district heating system over a period of more than 6 years. The advantages of the proposed forecasting methodology in terms of a higher forecast accuracy, its use for simulation purposes, or alternatively for better understanding transfer functions of district heating systems, are clearly shown.

Suggested Citation

  • Pinson, P. & Nielsen, T.S. & Nielsen, H.Aa. & Poulsen, N.K. & Madsen, H., 2009. "Temperature prediction at critical points in district heating systems," European Journal of Operational Research, Elsevier, vol. 194(1), pages 163-176, April.
    • Handle: RePEc:eee:ejores:v:194:y:2009:i:1:p:163-176
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    References listed on IDEAS

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    1. Dotzauer, Erik, 2002. "Simple model for prediction of loads in district-heating systems," Applied Energy, Elsevier, vol. 73(3-4), pages 277-284, November.
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    Cited by:

    1. Vogler–Finck, P.J.C. & Bacher, P. & Madsen, H., 2017. "Online short-term forecast of greenhouse heat load using a weather forecast service," Applied Energy, Elsevier, vol. 205(C), pages 1298-1310.
    2. Dobos, László & Abonyi, János, 2011. "Controller tuning of district heating networks using experiment design techniques," Energy, Elsevier, vol. 36(8), pages 4633-4639.
    3. Shamshirband, Shahaboddin & Petković, Dalibor & Enayatifar, Rasul & Hanan Abdullah, Abdul & Marković, Dušan & Lee, Malrey & Ahmad, Rodina, 2015. "Heat load prediction in district heating systems with adaptive neuro-fuzzy method," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 760-767.
    4. Zhong, Wei & Feng, Encheng & Lin, Xiaojie & Xie, Jinfang, 2022. "Research on data-driven operation control of secondary loop of district heating system," Energy, Elsevier, vol. 239(PB).
    5. Bergsteinsson, Hjörleifur G. & Sørensen, Mikkel Lindstrøm & Møller, Jan Kloppenborg & Madsen, Henrik, 2023. "Heat load forecasting using adaptive spatial hierarchies," Applied Energy, Elsevier, vol. 350(C).

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