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Analysis on the Technical Situation and Applied Difficulties of District Heating Load Forecasting

  • DISTRICT HEATING COGENERATION AND HEAT NETWORKS
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Abstract

As an effective means of energy use, district heating is widely used all over the world. The 4th generation of district heating further puts forward the concept of smart thermal grids and emphasizes the application of heating load prediction technology. This paper mainly reviews the three core elements of data preprocessing, modeling method and input eigenvalue, which affects the accuracy of heating load prediction. At the same time, based on the actual engineering investigation, the difficulties in the application of heating load forecasting in actual engineering are analyzed. On this basis, in order to promote the adoption rate of the 4th generation district heating, the application of heating load prediction in practical engineering is optimized from the following two aspects: enrich and perfect the heating load forecasting database to provide sufficient data support for the heating load forecasting; A partitioning scheme for data set construction of heating load prediction model is proposed in order to improve the accuracy of the prediction model. In order to prove the applicability of the proposed methods, the methods are applied to a district heating system in Kaifeng, and the results show that the prediction effect is obviously improved.

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Notes

  1. Multicollinearity is a close correlation between the factors selected for analysis, jointly affecting the overall result, which makes it difficult to assess the desired parameters.

  2. The dataset is a set of objects that store data from a database. Thanks to the creation of these sets, the corresponding data is updated and deleted without a permanent connection to the database.

REFERENCES

  1. M. Razmara, G. R. Bharati, D. Hanover, M. Shahbakhti, S. Paudyal, and R. D. Robinett, “Building-to-grid predictive power flow control for demand response and demand flexibility programs,” Appl. Energy 203, 128–141 (2017). https://doi.org/10.1016/j.apenergy.2017.06.040

    Article  Google Scholar 

  2. International Energy Agency, Transition to Sustainable Buildings (International Energy Agency, Paris, 2013).

    Book  Google Scholar 

  3. D. Connolly, H. Lund, B. V. Mathiesen, S. Werner, B. Möller, U. Persson, T. Boermans, D. Trier, P. A. Østergaard, and S. Nielsen, “Heat roadmap Europe: Combining district heating with heat savings to decarbonise the EU energy system,” Energy Policy 65, 475–489 (2014). https://doi.org/10.1016/j.enpol.2013.10.035

    Article  Google Scholar 

  4. M. Zheng, A. Knotzer, J. D. Billanes, and N. J. Bo, “A literature review of energy flexibility in district heating with a survey of the stakeholders' participation,” Renewable Sustainable Energy Rev. 123, 109750 (2020). https://doi.org/10.1016/j.rser.2020.109750

    Article  Google Scholar 

  5. A. H. Liu and Z. C. Ye, China Statistical Yearbook (China Statistics Press, Beijing, 2020) [in Chinese].

    Google Scholar 

  6. H. Lund, S. Werner, R. Wiltshir, Svendsen, and J. E. Thorsen, “4th Generation District Heating (4GDH): Integrating smart thermal grids into future sustainable energy systems,” Energy 68, 1–11 (2014). https://doi.org/10.1016/j.energy.2014.02.089

    Article  Google Scholar 

  7. Z. Y. Ma, J. Y. Xie, H. L. Li, Q. Sun, F. Wallin, Z. W. Si, and J. Guo, “Deep neural network-based impacts analysis of multimodal factors on heat demand prediction,” IEEE Trans. Big Data 6, 594–605 (2020). https://doi.org/10.1109/TBDATA.2019.2907127

    Article  Google Scholar 

  8. J. C. Guo, Y. Lan, P. N. Xue, Z. G. Zhou, and J. Liu, “Study on correction method for abnormal operation data of heating network,” Gas Heat 40, 8–13+41–42 (2020). https://doi.org/10.13608/j.cnki.1000-4416.2020.11.002

  9. J. Z. Q. Tian and M. P. Wang, “Heating load prediction based on support vector regression machine with parameters optimized by genetic algorithm,” Heat. Vent. Air Cond. 47 (2), 104–108 (2017).

    Google Scholar 

  10. S. J. Zhou, J. H. Pan, Q. F. Wang, Y. E. Zhao, and M. C. Tian, “Primary network operational optimization and algorithm of a district heating system,” J. Beijing Univ. Technol. 38, 628–635 (2012).

    Google Scholar 

  11. G. X. Xue, C. Y. Qi, H. Li, X. Kong, and J. C. Song, “Heating load prediction based on attention long short term memory: A case study of Xingtai,” Energy 203, 117846 (2020). https://doi.org/10.1016/j.energy.2020.117846

    Article  Google Scholar 

  12. P. N. Xue, Y. Jiang, Z. G. Zhou, X. Chen, X. M. Fang, and J. Liu, “Multi-step ahead forecasting of heat load in district heating systems using machine learning algorithms,” Energy 188, 116085 (2019). https://doi.org/10.1016/j.energy.2019.116085

    Article  Google Scholar 

  13. S. Idowu, S. Saguna, C. Ahlund, and O. Schelén, “Applied machine learning: Forecasting heat load in district heating system,” Energy Build. 133, 478–488 (2016). https://doi.org/10.1016/j.enbuild.2016.09.068

    Article  Google Scholar 

  14. S. Shamshirband, D. Petković, R. Enayatifar, A. H. Aboullah, D. Markovic, M. Lee, and R. Ahmad, “Heat load prediction in district heating systems with adaptive neuro-fuzzy method,” Renewable Sustainable Energy Rev. 48, 760–767 (2015). https://doi.org/10.1016/j.rser.2015.04.020

    Article  Google Scholar 

  15. K. Lang, M. Y. Zhang, and Y. B. Yuan, “Application of extreme learning machine with kernels model based on iterative error correction in short term electricity load forecasting,” J. Comput. Appl. 35, 2083–2087 (2015).

    Google Scholar 

  16. Z. Zhang, Y. Liu, L. Cao, and H. Si, “A forecasting method of district heat load based on improved wavelet neural network,” J. Energy Resour. Technol. 142, 102102 (2020). https://doi.org/10.1115/1.4047020

    Article  Google Scholar 

  17. M. Protić, S. Shamshirband, D. Petković, A. Abbasi, M. L. M. Kiah, J. A. Unar, L. Zivkovic, and M. Raos, “Forecasting of consumers heat load in district heating systems using the support vector machine with a discrete wavelet transform algorithm,” Energy 87, 343–351 (2015). https://doi.org/10.1016/j.energy.2015.04.109

    Article  Google Scholar 

  18. E. T. Al-Shammari, A. Keivani, S. Shamshirband, A. Mostafaeipour, P. L. Yee, D. Petkovic, and S. Ch, “Prediction of heat load in district heating systems by Support Vector Machine with Firefly searching algorithm,” Energy 95, 266–273 (2016). https://doi.org/10.1016/j.energy.2015.11.079

    Article  Google Scholar 

  19. X. J. Yu, J. H. Gu, C. Y. Qi, and C. H. Sun, “Comparison of several centralized heating load forecasting models,” Heat. Vent. Air Cond. 49 (2), 96–99 (2019).

    Google Scholar 

  20. J. Zhang, Q. Tian, and M. P. Wang, “Heating load prediction based on support vector regression machine with parameters optimized by genetic algorithm,” Heat. Vent. Air Cond. 47 (2), 104–108 (2017).

    Google Scholar 

  21. C. Johansson, M. Bergkvist, D. Geysen, O. De Somer, N. Lavesson, and D. Vanhoudt, “Operational demand forecasting in district heating systems using ensembles of online machine learning algorithms,” Energy Procedia 116, 208–216 (2017). https://doi.org/10.1016/j.egypro.2017.05.068

    Article  Google Scholar 

  22. Z. J. Tang, F. Ren, T. Peng, and W. B. Wang, “A least square support vector machine prediction algorithm for chaotic time series based on the iterative error correction,” Acta Phys. Sin. 63, 050505 (2014). https://doi.org/10.7498/aps.63.050505

    Article  Google Scholar 

  23. F. Dalipi, S. Y. Yayilgan, and A. Gebremedhin, “Data-driven machine-learning model in district heating system for heat load prediction: A comparison study,” Appl. Comput. Intell. Soft Comput. 2016, 3403150 (2016). https://doi.org/10.1155/2016/3403150

    Article  Google Scholar 

  24. M. Dahl, A. Brun, and G. B. Andresen, “Using ensemble weather predictions in district heating operation and load forecasting,” Appl. Energy 193, 455–465 (2017). https://doi.org/10.1016/j.apenergy.2017.02.066

    Article  Google Scholar 

  25. S. Sholahudin and H. Han, “Simplified dynamic neural network model to predict heating load of a building using Taguchi method,” Energy 115, 1672–1678 (2016). https://doi.org/10.1016/j.energy.2016.03.057

    Article  Google Scholar 

  26. D. Petković, M. Protić, S. Shamshirband, S. Akib, M. Raos, and D. Marković, “Evaluation of the most influential parameters of heat load in district heating systems,” Energy Build. 104, 264–274 (2015). https://doi.org/10.1016/j.enbuild.2015.06.074

    Article  Google Scholar 

  27. T. T. Fang and R. Lahdelma, “Evaluation of a multiple linear regression model and SARIMA model in forecasting heat demand for district heating system,” Appl. Energy 179, 544–552 (2016). https://doi.org/10.1016/j.apenergy.2016.06.133

    Article  Google Scholar 

  28. Y. F. Liu, X. X. Hu, X. Luo, Y. Zhou, D. J. Wang, and S. Farah, “Identifying the most significant input parameters for predicting district heating load using an association rule algorithm,” J. Cleaner Prod. 275, 122984 (2020). https://doi.org/10.1016/j.jclepro.2020.122984

    Article  Google Scholar 

  29. D. Popescu, F. Ungureanu, and A. Hernández-Guerrero, “Simulation models for the analysis of space heat consumption of buildings,” Energy 34, 1447–1453 (2009). https://doi.org/10.1016/j.energy.2009.05.035

    Article  Google Scholar 

  30. P.-F. Liu, R. Li, and Y. Wang, “Prediction of district heating load based on grey neural network model,” Heat. Vent. Air Cond. 49 (5), 124–128 (2019). https://doi.org/10.12783/dtcse/ammms2018/27287

    Article  Google Scholar 

  31. Y. Y. Wang, P. Wang, and Y. J. Duan, “Selection of water-mixing connection structures and power analysis of pumps in heating system,” Heat. Vent. Air Cond. 44 (9), 66–70 (2014).

    Google Scholar 

  32. N. Xu, C. L. Wu, Q. Fu, K. Chen, and L. H. Zhu, “Analysis on influencing factors of heating load based on grey correlation degree,” Build. Energy Environ. 38 (11), 19–22 (2019).

    Google Scholar 

  33. M. Protić, S. Shamshirband, M. H. Anisi, D. Petković, D. Mitić, M. Raos, M. Arir, and K. A. Alam, “Appraisal of soft computing methods for short term consumers' heat load prediction in district heating systems,” Energy 82, 697–704 (2015). https://doi.org/10.1016/j.energy.2015.01.079

    Article  Google Scholar 

  34. M. Macas, F. Moretti, A. Fonti, A. Giantomassi, G. Comodi, M. Annunziato, S. Pizzuti, and A. Capara, “The role of data sample size and dimensionality in neural network based forecasting of building heating related variables,” Energy Build. 111, 299–310 (2016). https://doi.org/10.1016/j.enbuild.2015.11.056

    Article  Google Scholar 

  35. T. H. Yuan, N. Zhu, Y. F. Shi, C. Chang, K. Yang, and Y. Ding, “Sample data selection method for improving the prediction accuracy of the heating energy consumption,” Energy Build. 158, 234–243 (2018). https://doi.org/10.1016/j.enbuild.2017.10.006

    Article  Google Scholar 

  36. A. Sandberg, F. Wallin, H. Li, and M. Azaza, “An analyze of long-term hourly district heat demand forecasting of a commercial building using neural networks,” Energy Procedia 105, 3784–3790 (2017). https://doi.org/10.1016/j.egypro.2017.03.884

    Article  Google Scholar 

  37. P. Jiang, B. G. Zhao, H. W. Zhang, L. F. Li, P. C. Wang, X. F. Wang, and W. X. Yuan, “Thermal load prediction model based on T-S fuzzy neural network,” Process Autom. Instrum. 40 (11), 20–23 (2019). https://doi.org/10.16086/j.cnki.issn1000-0380.2019010034

    Article  Google Scholar 

  38. Q. Ye, H. L. Sun, F. Q. Kong, Y. F. Wang, Q. Zhao, W. Zhong, F. J. Li, and G. T. Ju, “Study and application of intelligent dispatch technology for steam heating networks in industrial parks,” Huadian Technol. 42 (11), 6–13 (2020).

    Google Scholar 

  39. J. Y. Xie, H. L. Li, Z. Y. Ma, Q. Sun, F. Wallin, Z. W. Si, and J. Guo, “Analysis of key factors in heat demand prediction with neural networks,” Energy Procedia 105, 2965–2970 (2017). https://doi.org/10.1016/j.egypro.2017.03.704

    Article  Google Scholar 

  40. P. Potočnik, E. Strmčnik, and E. Govekar, “Linear and neural network-based models for short-term heat load forecasting,” Strojniski Vestn. – J. Mech. Eng. 61, 543–550 (2015). https://doi.org/10.5545/sv-jme.2015.2548

    Article  Google Scholar 

  41. B. W. Zhao, W. Li, and Y. Jin, “Heating load prediction based on PSO-LSSVM,” Build. Energy Effic. 49 (6), 46–49 (2021).

    Google Scholar 

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Funding

The work was supported by the Research Foundation of Education Bureau of Zhejiang Province, China (Grant no. Y202147916).

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Authors and Affiliations

  1. Zhejiang Sci-Tech University, Xiasha Higher Education Zone, 310018, Zhejiang, Hangzhou, P.R. China

    Bingwen Zhao, Yu Jin, Wan Li & Hanyu Zheng

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  1. Bingwen Zhao
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  2. Yu Jin
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Correspondence to Yu Jin.

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Zhao, B., Jin, Y., Li, W. et al. Analysis on the Technical Situation and Applied Difficulties of District Heating Load Forecasting. Therm. Eng. 69, 464–472 (2022). https://doi.org/10.1134/S0040601522060088

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