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Research on thermal load prediction of district heating station based on transfer learning

Author

Listed:
  • Wang, Chendong
  • Yuan, Jianjuan
  • Huang, Ke
  • Zhang, Ji
  • Zheng, Lihong
  • Zhou, Zhihua
  • Zhang, Yufeng

Abstract

Precise prediction of thermal load plays a critical role in China to fulfill the demand of energy saving, carbon emission reduction and environmental protection, and to realize the "3060″ target. This study proposed layer transfer model and merged transfer model for thermal load prediction of the district heating station. Experiment schemes were elaborated to simulate cross-year and cross-site scenarios, and practical data was collected serving the experiments. The prediction accuracy can be maintained without degradation in cross-year scenario, specifically, the coefficient of variation of the root mean squared error fluctuated between −1.09% and +0.45% compared to previous heating season when proposed two models were used. In the cross-site scenario, proposed models can achieve good prediction performance when the training data is insufficient. The coefficient of variation of the root mean squared error of the new model with insufficient training data was reduced by 7.62% on average when merged transfer model was used, which is equivalent to an overall reduction of 41.67%. Furthermore, proposed models can be applied to further optimize the prediction performance, even if beyond the scenarios discussed in this study.

Suggested Citation

  • Wang, Chendong & Yuan, Jianjuan & Huang, Ke & Zhang, Ji & Zheng, Lihong & Zhou, Zhihua & Zhang, Yufeng, 2022. "Research on thermal load prediction of district heating station based on transfer learning," Energy, Elsevier, vol. 239(PE).
    • Handle: RePEc:eee:energy:v:239:y:2022:i:pe:s0360544221025573
    DOI: 10.1016/j.energy.2021.122309
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    References listed on IDEAS

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    Cited by:

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    3. Li, Tailu & Zhang, Yao & Wang, Jingyi & Jin, Fengyun & Gao, Ruizhao, 2024. "Techno-economic and environmental performance of a novel thermal station characterized by electric power generation recovery as by-product," Renewable Energy, Elsevier, vol. 221(C).
    4. Li, Jiangkuan & Lin, Meng & Li, Yankai & Wang, Xu, 2022. "Transfer learning network for nuclear power plant fault diagnosis with unlabeled data under varying operating conditions," Energy, Elsevier, vol. 254(PB).
    5. Kangji Li & Borui Wei & Qianqian Tang & Yufei Liu, 2022. "A Data-Efficient Building Electricity Load Forecasting Method Based on Maximum Mean Discrepancy and Improved TrAdaBoost Algorithm," Energies, MDPI, vol. 15(23), pages 1-18, November.

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