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Heat demand forecasting algorithm for a Warsaw district heating network

Author

Listed:
  • Kurek, Teresa
  • Bielecki, Artur
  • Świrski, Konrad
  • Wojdan, Konrad
  • Guzek, Michał
  • Białek, Jakub
  • Brzozowski, Rafał
  • Serafin, Rafał

Abstract

This paper presents a complex analysis of heat demand forecasting methods for the Warsaw District Heating Network, which is owned by Veolia Energia Warszawa, the largest district heating network (DHN) in the European Union. The analyzed network supplies heat for both domestic and heating purposes. Therefore, summer, intermediate, and winter seasons were delineated and separately evaluated. Numerous models were utilized including models broadly recognized and used (ridge regression, autoregression with exogenous input, deep artificial neural networks), as well as previously unexplored models (combination of summer and winter linear models with the utilization of fuzzy logic). A 72 h forecast horizon is evaluated for total heat demand (the sum of all substations), as well as for groups of buildings (local models for specific city areas), and individually for the majority of substations. Models of areas use an additional input variable, namely, the results of the total heat demand forecast, and are proposed to be developed as an auxiliary information variable offered to grid operators. An artificial neural network based model achieves the best accuracy for all analyzed seasons. The intermediate seasons prove to be the most difficult to accurately forecast for and only the combination of summer and winter linear autoregresive models with utilization of a fuzzy logic reached comparable accuracy.

Suggested Citation

  • Kurek, Teresa & Bielecki, Artur & Świrski, Konrad & Wojdan, Konrad & Guzek, Michał & Białek, Jakub & Brzozowski, Rafał & Serafin, Rafał, 2021. "Heat demand forecasting algorithm for a Warsaw district heating network," Energy, Elsevier, vol. 217(C).
    • Handle: RePEc:eee:energy:v:217:y:2021:i:c:s0360544220324543
    DOI: 10.1016/j.energy.2020.119347
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    Cited by:

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    2. Szabolcs Kováč & German Micha’čonok & Igor Halenár & Pavel Važan, 2021. "Comparison of Heat Demand Prediction Using Wavelet Analysis and Neural Network for a District Heating Network," Energies, MDPI, vol. 14(6), pages 1-20, March.
    3. Seul-Ye Lim & Jeoung-Sik Min & Seung-Hoon Yoo, 2021. "Price and Income Elasticities of Residential Heat Demand from District Heating System: A Price Sensitivity Measurement Experiment in South Korea," Sustainability, MDPI, vol. 13(13), pages 1-10, June.
    4. Runge, Jason & Saloux, Etienne, 2023. "A comparison of prediction and forecasting artificial intelligence models to estimate the future energy demand in a district heating system," Energy, Elsevier, vol. 269(C).
    5. Chen, Minghao & Xie, Zhiyuan & Sun, Yi & Zheng, Shunlin, 2023. "The predictive management in campus heating system based on deep reinforcement learning and probabilistic heat demands forecasting," Applied Energy, Elsevier, vol. 350(C).
    6. Manservigi, Lucrezia & Bahlawan, Hilal & Losi, Enzo & Morini, Mirko & Spina, Pier Ruggero & Venturini, Mauro, 2022. "A diagnostic approach for fault detection and identification in district heating networks," Energy, Elsevier, vol. 251(C).
    7. Gong, Mingju & Zhao, Yin & Sun, Jiawang & Han, Cuitian & Sun, Guannan & Yan, Bo, 2022. "Load forecasting of district heating system based on Informer," Energy, Elsevier, vol. 253(C).
    8. Chung, Won Hee & Gu, Yeong Hyeon & Yoo, Seong Joon, 2022. "District heater load forecasting based on machine learning and parallel CNN-LSTM attention," Energy, Elsevier, vol. 246(C).
    9. Chicherin, Stanislav & Starikov, Aleksander & Zhuikov, Andrey, 2022. "Justifying network reconstruction when switching to low temperature district heating," Energy, Elsevier, vol. 248(C).
    10. Pavel Rušeljuk & Kertu Lepiksaar & Andres Siirde & Anna Volkova, 2021. "Economic Dispatch of CHP Units through District Heating Network’s Demand-Side Management," Energies, MDPI, vol. 14(15), pages 1-20, July.

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