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Appraisal of soft computing methods for short term consumers' heat load prediction in district heating systems

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  • Protić, Milan
  • Shamshirband, Shahaboddin
  • Anisi, Mohammad Hossein
  • Petković, Dalibor
  • Mitić, Dragan
  • Raos, Miomir
  • Arif, Muhammad
  • Alam, Khubaib Amjad

Abstract

District heating systems can play a significant role in achieving stringent targets for CO2 emissions with concurrent increase in fuel efficiency. However, there are numerous possibilities for future improvement of their operation. One of the potential domains is control, where short-term prediction of heat load can play a significant role. With reliable prediction of consumers' heat consumption, production could be altered to match the real consumers' needs. This will have an effect on lowering the distribution cost, heat losses, and especially primary and secondary return temperatures, which will consequently result in increased overall efficiency of district heating systems. This paper compares the accuracy of different predictive models of individual consumers in district heating systems. For that purpose, we designed and tested numerous models based on the SVR (support vector regression) with a polynomial (SVR–POLY) and a radial basis function (SVR–RBF) as the kernel functions, with different set of input variables and for four prediction horizons. Model building and testing was performed using experimentally obtained data from one heating substation. The results were compared using the RMSE (root-mean-square error) and the coefficient of determination (R2). The prediction results of SVR–POLY models outperformed the results of SVR–RBF models for all prediction horizons and all sampling intervals. Moreover, the SVR–POLY demonstrated high generalization ability, so we propose that it should be used as a reliable tool for the prediction of consumers' heat load in DHS (district heating systems).

Suggested Citation

  • Protić, Milan & Shamshirband, Shahaboddin & Anisi, Mohammad Hossein & Petković, Dalibor & Mitić, Dragan & Raos, Miomir & Arif, Muhammad & Alam, Khubaib Amjad, 2015. "Appraisal of soft computing methods for short term consumers' heat load prediction in district heating systems," Energy, Elsevier, vol. 82(C), pages 697-704.
    • Handle: RePEc:eee:energy:v:82:y:2015:i:c:p:697-704
    DOI: 10.1016/j.energy.2015.01.079
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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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    1. Gu, Jihao & Wang, Jin & Qi, Chengying & Min, Chunhua & Sundén, Bengt, 2018. "Medium-term heat load prediction for an existing residential building based on a wireless on-off control system," Energy, Elsevier, vol. 152(C), pages 709-718.
    2. Xue, Puning & Jiang, Yi & Zhou, Zhigang & Chen, Xin & Fang, Xiumu & Liu, Jing, 2019. "Multi-step ahead forecasting of heat load in district heating systems using machine learning algorithms," Energy, Elsevier, vol. 188(C).
    3. Xu, Lei & Hou, Lei & Zhu, Zhenyu & Li, Yu & Liu, Jiaquan & Lei, Ting & Wu, Xingguang, 2021. "Mid-term prediction of electrical energy consumption for crude oil pipelines using a hybrid algorithm of support vector machine and genetic algorithm," Energy, Elsevier, vol. 222(C).
    4. Yuan, Jianjuan & Wang, Chendong & Zhou, Zhihua, 2019. "Study on refined control and prediction model of district heating station based on support vector machine," Energy, Elsevier, vol. 189(C).
    5. 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.
    6. Huang, Ke & Yuan, Jianjuan & Zhou, Zhihua & Zheng, Xuejing, 2022. "Analysis and evaluation of heat source data of large-scale heating system based on descriptive data mining techniques," Energy, Elsevier, vol. 251(C).
    7. Sun, Chunhua & Liu, Yanan & Gao, Xiaoyu & Wang, Jinda & Yang, Lan & Qi, Chengyong, 2022. "Research on control strategy integrated with characteristics of user's energy-saving behavior of district heating system," Energy, Elsevier, vol. 245(C).
    8. Xue, Puning & Zhou, Zhigang & Fang, Xiumu & Chen, Xin & Liu, Lin & Liu, Yaowen & Liu, Jing, 2017. "Fault detection and operation optimization in district heating substations based on data mining techniques," Applied Energy, Elsevier, vol. 205(C), pages 926-940.
    9. Sholahudin, S. & Han, Hwataik, 2016. "Simplified dynamic neural network model to predict heating load of a building using Taguchi method," Energy, Elsevier, vol. 115(P3), pages 1672-1678.
    10. Ahn, Jonghoon & Chung, Dae Hun & Cho, Soolyeon, 2018. "Energy cost analysis of an intelligent building network adopting heat trading concept in a district heating model," Energy, Elsevier, vol. 151(C), pages 11-25.

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